research paper in landslide

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Worldwide Research Trends in Landslide Science

Paúl carrión-mero.

1 Centro de Investigaciones y Proyectos Aplicados a las Ciencias de la Tierra (CIPAT), Campus Gustavo Galindo, ESPOL Polytechnic University, Km 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador; ce.ude.lopse@vlatnomn (N.M.-B.); ce.ude.lopse@etnaromf (F.M.-C.)

2 Facultad de Ingeniería en Ciencias de la Tierra, Campus Gustavo Galindo, ESPOL Polytechnic University, Km 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador

Néstor Montalván-Burbano

3 Department of Economy and Business, University of Almería, Ctra. Sacramento s/n, 04120 La Cañada de San Urbano, Spain

Fernando Morante-Carballo

4 Facultad de Ciencias Naturales y Matemáticas (FCNM), Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador

5 Geo-Recursos y Aplicaciones (GIGA), Campus Gustavo Galindo, ESPOL Polytechnic University, Km. 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador

Adolfo Quesada-Román

6 Department of Geography, University of Costa Rica, San José 2060, Costa Rica; [email protected]

Boris Apolo-Masache

Associated data.

Not applicable.

Landslides are generated by natural causes and by human action, causing various geomorphological changes as well as physical and socioeconomic loss of the environment and human life. The study, characterization and implementation of techniques are essential to reduce land vulnerability, different socioeconomic sector susceptibility and actions to guarantee better slope stability with a significant positive impact on society. The aim of this work is the bibliometric analysis of the different types of landslides that the United States Geological Survey (USGS) emphasizes, through the SCOPUS database and the VOSviewer software version 1.6.17, for the analysis of their structure, scientific production, and the close relationship with several scientific fields and its trends. The methodology focuses on: (i) search criteria; (ii) data extraction and cleaning; (iii) generation of graphs and bibliometric mapping; and (iv) analysis of results and possible trends. The study and analysis of landslides are in a period of exponential growth, focusing mainly on techniques and solutions for the stabilization, prevention, and categorization of the most susceptible hillslope sectors. Therefore, this research field has the full collaboration of various authors and places a significant focus on the conceptual evolution of the landslide science.

1. Introduction

Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life globally [ 1 ]. Mass movement processes cause significant changes in the Earth’s relief, causing economic losses due to landslides in mountainous areas with a dense population [ 2 , 3 ], and even in the direct and indirect cost of buildings or infrastructure on an urban scale [ 4 , 5 , 6 ].

In the evolution of the reliefs, landslides are considered to be intrinsic processes, and among other dynamics, they favor the formation of valleys [ 7 ], and the contribution of river sediments and ecological renewal. The degree of physical, biological and chemical weathering, earthquakes, and extraordinary rains (among other natural processes) can cause slope instability [ 8 , 9 ].

Landslides have caused costly damage and loss of life worldwide, yet the most devastating disasters occur in developing countries [ 10 ]. Therefore, the implementation of techniques to reduce geological risks and natural vulnerability is essential for developing disaster prevention and mitigation strategies on various scales [ 11 , 12 , 13 , 14 ].

This research field has different approaches and objectives that have evolved over the last decades [ 15 ]. Some studies have been based on satellite images in remote sensing [ 16 ], geomorphological mapping [ 17 , 18 ], its relationship with earthquakes [ 9 ], continuous monitoring of places susceptible to landslides [ 19 , 20 ], triggering of landslides due to extraordinary precipitation events [ 21 , 22 , 23 ] and various methods for stabilizing slopes [ 24 , 25 ].

There are other studies of a preventive nature, such as real-time warnings of landslides due to the action of rains in winter [ 26 ] and in unsaturated areas above the water table [ 27 ], which are of great support for adequate management of these disasters. The consequences caused by landslides (centralized in an environmental and socioeconomic framework) show that their impacts have greater intensity in areas with higher population density [ 28 ]. Across the world, there is a great number of landslides that have affected the population from cold, temperate and tropical regions [ 13 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ].

According to the United States Geological Survey (USGS), the material involved in a landslide and its type of mass movement is a significant basis for the classification of landslides [ 36 ]. Therefore, given the internal mechanics that predominates in mass movements, the landslides are classified as: falls, topples, slides, spreads, and flows ( Figure 1 ).

Classification scheme based on the literature review of the USGS landslide manual. Source: [ 36 ].

The academic field of landslides is broad, where some researchers have made efforts to understand their structure [ 37 ], addressing literature reviews [ 11 ] and their classification [ 36 , 38 , 39 ], as well as the bibliometric analysis of various landslide concepts through the Science Citation Index-Expanded (SCIE) and Social Sciences Citation Index (SSCI) databases (1991–2014) [ 13 ]. Over time, various studies have been carried out regarding landslides, but very few have highlighted their structure and intellectual growth. Therefore, a new bibliometric study would allow a new approach to its structure and updates on its different research scopes.

The use of bibliometric methods is considered for the analysis of scientific activity in an academic field. Derek J. de Solla Price initially exhibited the bibliometric analysis in 1965 [ 40 ]. The proposal focuses on the quantitative evaluation of an academic field of study by analyzing its structure, characteristics and existing relationships, which allows examining its behaviour between the disciplines of a specific field of study [ 41 , 42 ]. The bibliometric analysis allows identifying research areas (current and future) and the analysis of their multidisciplinary production, achieving a more systematic comprehensive evaluation in the field of study [ 43 , 44 ].

Due to the above, the research question arises: How has the intellectual/conceptual structure of the various types of landslides developed over time?

The present study aims to evaluate the intellectual structure of the landslide through performance analysis and bibliometric mapping to determine the development, patterns and trends of its scientific structure. Thus, to analyze the scientific production and intellectual structure of the field of study, managing to provide a transparent, updated, reliable and high-quality study for its transdisciplinary use.

This study has been structured in five sections, starting with an introductory framework of the problem, highlighting its objective and investigative question to support at the end of this work, followed by Section 2 , in which the materials and the implemented methodology are described (three phases: research criteria and source identification, software and data extraction, and data analysis and interpretation). Section 3 represents the results and their analysis, to later be discussed in Section 4 and, finally, Section 5 concludes with the scientific trends of this research field.

2. Materials and Methods

A systematic review allows an exploration of the intellectual territory of existing studies in the face of a problem raised, evaluating the contributions and synthesizing the data obtained to provide reliable knowledge of a particular field of study [ 45 , 46 ]. This exhaustive and rigorous procedure is similar to the protocol presented in the bibliometric analysis [ 47 , 48 ].

The bibliometric analysis allows evaluating the scientific production of journals [ 49 , 50 ] or understanding the intellectual structure of various fields of knowledge such as management [ 51 , 52 , 53 ], environment [ 54 , 55 , 56 ], natural science [ 57 ] and health [ 58 ]. Employing analytical techniques that allow an exploration of the tendencies of investigation and interpretation of new perspectives in the investigative field [ 59 , 60 ].

The methodology proposed in this work is shown in Figure 2 . Its structure comprises three phases that allow the proposed bibliometric analysis to be carried out: (i) Research criteria; (ii) reprocessing of data and software; and (iii) analysis and interpretation of data.

Bibliometric research methodology applied in this study.

2.1. Phase I. Research Criteria and Database Use

For this research, a bibliographic search of the classification of landslides was established based on the internal mechanics of the mass movement. These requirements are encompassed by the USGS, which establishes a classification according to the internal mechanics present in landslides, such as fall, topple, slide, spread and flow [ 36 ]. The selection of these terms allows the compilation of the base documents to be considered in this study.

The selection of documents should be made based on choosing a reliable, quality database with comprehensive coverage. The databases used for bibliometric studies are the Web of Science and Scopus, which differ in volume of information, journal coverage and subject areas [ 61 ]. The Scopus database was selected due to its comprehensive coverage in years, journals in various areas of knowledge [ 62 , 63 , 64 , 65 ], an intuitive search system, easy data download and high-quality standards [ 66 , 67 ], which allows a more precise bibliometric evaluation in the domain of any subject to be analyzed.

The search carried out in Scopus focuses on the titles of the publications that contain the term “landslide” with the terms of: fall, fall, slide, spread and flow. The search topic is as follows: (TITLE (fall*) OR TITLE (topple*) OR TITLE (slide*) OR TITLE (spread*) OR TITLE (flow*) AND TITLE (landslide*)).

The landslide research field is vast, so it is necessary to obtain more exact results and synthesize the study approach; therefore, the search in Scopus focuses only on the title of the publications with the previously established terms [ 68 , 69 ]. In this way, a total of 661 publications were obtained, to which inclusion criteria such as all types of document, language, years and study area were applied [ 13 ], in addition to an exclusion criterion such as the year 2021 (year still in progress), obtaining a final database of 641 documents.

2.2. Phase II. Data and Software Reprocessing

The selected records are downloaded in csv format (comma separated values) from the Scopus database for analysis using the Microsoft Excel software from Office 365 ProPlus [ 70 ]. Since the downloaded database contains miles of data from various variables (e.g., authors name, document title, year, keywords, abstracts, among others), a review and cleaning of the data is required to ensure precision in analysis results [ 71 , 72 ]. Cleaning consists of eliminating duplicated values, incomplete or erroneous records that cannot be completed manually [ 73 ]. A total of 9 deleted records and 632 documents to be analyzed were established.

The new csv files were entered in VOSviewer, an open access and reliable software that allows the construction and visualization of bibliometric networks in various fields of study, allowing a comprehensive bibliometric mapping in any research branch [ 74 , 75 ]. This software allows an analysis of the structure of the research field through co-occurrence [ 76 ], co-citations [ 77 , 78 , 79 , 80 ], and bibliographic coupling [ 81 ]. This software has been used in different scientific areas such as: sustainability [ 82 ], natural and cultural resources [ 83 ], geosciences [ 55 , 84 ], medicine [ 76 ] and the circular economy [ 85 ], among others. Its analysis is carried out only for articles in English, obtaining a total of 354 documents.

2.3. Phase III. Data Analysis and Interpretation

The results were examined using the two classic approaches to bibliometric analysis: Performance Analysis and Science Mapping [ 42 , 86 ].

• Performance analysis allows an evaluation of its scientific production (authors, countries, journals) and its scientific impact [ 87 , 88 ];
• sciences mapping allows the graphic representation of the cognitive structure of the study field and its evolution [ 41 , 89 ]. It is considered to apply a triangulation method that allows an analysis of this structure by examining its micro (keywords), meso (articles and authors) and macro (journals) components [ 90 ].

3.1. Performance Analysis

3.1.1. scientific production.

From 1952 to 1990 ( Figure 3 ), landslides have been analyzed from a descriptive perspective, considering the internal mechanics and the mass movement type that is generated according to the lithology and the material involved [ 91 , 92 , 93 ]. Its leading causes are determined, such as the hydraulic gradient and earthquakes [ 94 , 95 , 96 , 97 ]. There is also the beginning of geotechnical and geomorphological studies and the elaboration of models to understand the internal mechanics of the different triggered landslides [ 93 , 98 , 99 ]. Given this analysis, this period is considered to be the beginning of studies that will be the basis for further research.

Growth of scientific production of landslides.

Figure 3 shows a progressive growth in 1990–2020, determining three different periods that frame the studies.

Period I (1990–2000) focuses on researches related to the debris flows, managing to generate models for the understanding and prediction of landslides, and the volume of material deposited in a sector [ 100 , 101 ]. It considers different aspects such as the mechanical process of mass movement [ 102 , 103 ], data in the field (rainfall, vegetation cover, slope inclination, distance, elevation), coefficient of internal friction, among others [ 104 , 105 , 106 , 107 ]. This period is the basis for continuous studies and analysis of future landslide models.

In period II (2001–2010), the exponential research growth and a significant focus on the classification of landslides is observed. These classifications focus on the area of engineering and speed of landslide for the elaboration of physical models [ 108 ], considering the material involved (gravel, sand, silt and clay) and its variations (debris, earth and mud, peat and rock), thus managing to formalize definitions that allow identifying the present types of landslides [ 109 , 110 , 111 , 112 ]. In 2008, a relevant study to the global analysis of rainfall was presented, which made it possible to study rainfall and its influence on shallow landslides and debris flows [ 113 ]. These studies are the basis of all landslide warning systems throughout the world [ 114 , 115 , 116 ]. From this, the mathematical prediction models have been considered of great importance worldwide, calculating and predicting the trajectory, speed and depth that landslides would have [ 117 , 118 , 119 ].

Finally, period III (2011–2020) focuses on the improvement and combination of different numerical models, managing to represent the reality of the environment and the mechanical behavior of the landslides for their respective analysis in field and risk assessment [ 120 , 121 , 122 , 123 ]. In this way, at the end of this period, these investigations and improved models allow us to understand the behavior of different landslides types [ 124 , 125 , 126 ]. In addition, the geomorphological, tectonic and hydrodynamic processes involved in mass movement processes were explained in detail [ 127 , 128 ]. Different experimental research was conducted considering the pressure of the pore fluid, type of grain, rainfall and a large amount of on-site and laboratory investigations, assuring the validity of the results [ 129 , 130 , 131 , 132 , 133 , 134 ].

3.1.2. Language and Types of Documents

In the areas of knowledge related to Life Science and Earth Science, the English language is predominant [ 135 ]. Landslide is no exception; despite presenting studies in 15 languages, 81.8% of its studies are written in English. This predilection for language is due to its relevance in scientific communication as there is an overrepresentation of English-speaking journals, and it is the common nexus for international collaboration [ 136 , 137 ]. The second language is Chinese (13.45%), due to its high national collaboration on topics of debris flow and flow-type landslides in national indexed journals (e.g., Yantu Lixue/Rock and Soil Mechanics, Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, Journal of Natural Disasters).

Another characteristic of landslide studies is that they mostly constitute journal articles (74%) since these documents are considered certified knowledge, as they are examined by peer reviewers who have expertise in the field of knowledge [ 138 ]. Other types of documents are shown in Figure 4 .

Types of scientific publications.

3.1.3. Contribution by Country

The analysis of the contribution of the countries allows us to understand their relationships in knowledge generation [ 87 ]. This product is developed by the collaboration of 64 countries (see Figure 5 ), in which most of the research is related to developed countries. The map was generated through ArcMap 10.5 software, using data from the authors’ affiliations.

Contribution by countries, world map.

China has the most significant academic contribution on landslides ( Figure 5 ), collaborating with 47 countries, especially Italy, the United Kingdom and the United States. The contributions with Italy are related to numerical modelling in the propagation of flow-like landslides [ 139 , 140 , 141 ]. Concerning the United Kingdom, studies focus on modelling debris flow and submarine landslides and as a flow influenced by precipitation, earthquakes, or tectonic movements, e.g., [ 142 , 143 , 144 ]. The third international partner, the United States, focuses on landslide monitoring and numerical modelling based on the smoothed particle hydrodynamics (sph) method, e.g., [ 145 , 146 , 147 ]. China has experienced sustained economic growth over the last 30 years, allowing broad knowledge development in various academic fields [ 148 ].

In Italy, as the second country with more contributions in the analyzed topic, representative authors such as Guzzetti F., Cuomo S., Cascini L., Sorbino G., Crosta G.B. present studies focused on numerical modelling, the application of sph and GEOtop-FS, run-out analysis and trigger factors in shallow landslides and debris flows [ 117 , 118 , 119 , 149 , 150 ]. Japan is the third country with a scientific contribution, with authors such as Imaizumi F., Sassa K., Wuang G. who highlight the effects of landslides and shallow landslides as a consequence of deforestation, groundwater flow, earthquakes, rainfall and flow path [ 151 , 152 , 153 , 154 , 155 ]. Other countries contributing in this area can be observed in Figure 5 .

3.2. Bibliometric Mapping Analysis

The construction of bibliometric maps, depending on what is established in the methodology. Only articles and the English language are considered given their broad domain in various areas of knowledge [ 156 , 157 ].

3.2.1. Co-Occurrence Author Keyword Network

This type of analysis allows visualizing the study area (its history and evolution) and its possible trends [ 158 , 159 , 160 ].

Figure 6 shows the co-occurrence network of author keywords, where 25 nodes (represents each author-keyword with at least four co-occurrences) and four clusters (groupings of nodes of the same color) are observed [ 161 ]. The figure allows a visualization of the intellectual structure of landslides to be examined in greater detail.

Visualization of the co-occurrence network by assigning a representative color for each cluster. Red color (shallow landslide), green color (flow like landslide), blue color (debris flow) and yellow color (landslide).

Cluster 1 (red color) shows studies of landslides caused by precipitation and pore pressure in the subsoil studied, due to the topography and water flow caused by rainfall [ 94 , 115 , 162 , 163 , 164 ]. These studies were carried out based on: (i) post-failure in deposits of colluvial, weathered and pyroclastic origin [ 118 ]; (ii) simulation of the probability of occurrence in hydrographic basins using GEOtop-FS [ 117 ]; (iii) the quantification of morphology and hydrological conditions [ 165 ]; and (iv) an evaluation of susceptibility and slope stability for landslide prevention [ 166 ]. Other studies reflect the slope instability that can cause significant hazards, mainly influenced by the deposit type, the rapid flows generated by seismic movements [ 167 , 168 , 169 ], large-scale deforestation [ 170 ], groundwater fluctuation, and different triggering scenarios [ 132 , 171 ].

Studies focusing on this cluster have led to improved mapping, understanding, interpretation and prediction of landslides, such as the movement direction through the hydraulic gradient [ 172 ], the influence of rainfall, soil saturation [ 125 , 173 ] and continuous monitoring for preventive decisions in potential hazardous landslides [ 174 ].

Cluster 2 (green color) focuses on landslides with a non-Newtonian flow behavior, demonstrated through numerical modelling, geological study and its geodynamic behavior [ 121 , 175 , 176 , 177 ]. These movements and trajectories are influenced by different factors such as: (i) rheology and topography [ 139 ]; (ii) hydrometeorological events such as heavy rainfall [ 113 , 178 ]; (iii) soil saturation in gravelly and sandy materials [ 178 ]; (iv) pore pressure impact caused by earthquakes [ 155 , 179 , 180 ]; and (v) the frontal plowing phenomenon [ 140 ]. These landslides have a natural, rapid and irregular behavior with devastating dynamics. This cluster provides the scientific community with resources to understand flow-like landslides through numerical and 3D models [ 181 ]. Models considering the smoothed particle hydrodynamics (SPH) [ 77 , 182 , 183 , 184 ] and the use of satellite images using methods such as InSAR [ 185 , 186 , 187 ]. These studies have allowed the modelling of submarine landslides [ 188 , 189 ] and landslides in landfills caused by seismic action [ 182 ]. In addition, they facilitate the affected area mapping and evaluate the intensity of the danger for the planning of adequate risk management [ 190 ].

Cluster 3 (blue color), these landslides can be generated by: (i) earth rubble and intense added rainfall [ 131 , 191 ] or when they come in contact with the mainstream [ 116 ]; (ii) failures in the landslide dam [ 192 , 193 ]; and (iii) the material traction on a slope, liquefaction or even due to temperature changes [ 105 ]. For its understanding, various experiments were carried out, such as the use of differential equations for the dynamics of the system [ 129 ], analysis of the theory of the critical state in the mobilization of debris flows due to the increase in the basal pressure of pores [ 194 ], and the generation of dynamic models to understand the evolution of the system [ 112 ]. For a further understanding of debris flow, maps used that are supported by Geographic Information Systems (GIS) [ 195 , 196 ], geophysical studies [ 197 ] and statistical methods such as logistic regression (LR) [ 198 , 199 ] and Multivariate Adaptive Regression Splines (MARS) were explored [ 200 ], allowing us to understand the formation or prevention of landslide dams [ 201 , 202 , 203 ] and debris flows, which can also be generated by shallow landslides, which are identified through susceptibility mapping [ 124 , 204 , 205 ].

Cluster 4 (yellow color), covers the topics written in other clusters given its great diversity or classification [ 36 ]. Its studies focus on numerical simulations for the understanding and prediction of landslides [ 206 , 207 , 208 ], which allows an understanding of the groundwater flow affectation [ 209 , 210 ], the infiltration of water by rainfall [ 211 , 212 ] and wave propagation (tsunamis) due to the collapse of slopes in bodies of water [ 181 , 213 ]. Recently, scientific contributions regarding landslides have been present. Multiphase flow models present submarine landslides, especially on the type and size of particles (rheology) [ 188 ]. Regarding groundwater or what is percolated by high rainfall, it is considered in Critical Rainfall Threshold (CRT) analysis, monitoring system by video camera systems and the generation of two-dimensional mathematical models by the finite difference method [ 214 , 215 , 216 ].

3.2.2. Co-Citation Analysis

Co-citation analysis is one of the most widely used methods in bibliometric analysis [ 41 ]. It allows us to explore the relationships between documents, to know the knowledge base and the intellectual structure of a field of study [ 217 , 218 ]. Co-citation analyzes the number of times two documents are co-cited by another subsequent document [ 79 ]. When frequently cited in other publications, documents show a close relationship, which allows us to consider that they belong to the same field of research [ 219 , 220 ]. However, this relevance does not imply that the ideas shared by the various authors coincide with each other [ 221 ].

In this work, two co-citation methods are used: author co-citation analysis and Journal co-citation analysis, which are presented below:

Author Co-Citation Analysis (ACA)

This analysis is an adaptation of work by H. Small [ 79 ], done by White and Griffith [ 222 ] using the authors of the papers. ACA considers that by citing two authors more frequently in several papers, it is very likely that their fields of research are similar [ 223 ]. This makes it possible to discover the co-citation groups of reference authors that make up the knowledge base of the intellectual structure studied [ 73 , 224 ]. Furthermore, it allows the discovery of the academic community linked to confirming this knowledge base [ 225 ].

Figure 7 shows this co-citation network of authors. Its construction is carried out with the VOSviewer software version 1.6.17, which uses a proprietary technique called VOS to allow a grouping of the units of analysis using similarities [ 74 ]. The nodes represent the authors’ names, which may represent topics, schools of thought or specialties [ 226 ]. The structure presents six clusters, with 235 authors possessing more than 20 co-citations.

Visualization of the co-citation network assigning a representative color for each cluster. according to the number of interconnected authors. Red, green, blue, yellow, purple and light blue (in order of highest importance by VOSviewer software version 1.6.17).

Cluster 1 (red color) consists of 60 authors. The studies in this cluster focus on the research area of shallow landslides and debris flow influenced by rainfall or hydrological triggers [ 227 , 228 , 229 ]. These authors include Guzzetti F. (157 co-citations), in studies related to precipitation and shallow landslides [ 113 , 230 ]; Crosta G.B. (128) in numerical modelling and debris flow [ 231 , 232 ]; and Godt J.W. (107), in map generation and modelling of shallow landslides for landslide risk prevention and assessment [ 233 , 234 ].

Cluster 2 (green color) has 44 authors. This cluster has studies focused on the internal mechanics of landslides and debris flows, and the factors that affect the movement or detachment of material [ 235 , 236 , 237 , 238 , 239 ], in addition, it considers the run-out analysis of rock and soil slides [ 121 , 240 , 241 ]. These research topics are cover by various authors such as Sassa K., Xu Q and Wang G. with 131, 97 and 90 citations.

Cluster 3 (blue color) consists of 39 authors, some of the authors, such as: Pastor M. (126), consider the stabilization of slopes using models [ 119 , 242 , 243 , 244 ], while Cascini L. (122) and Evans S.G. (115), focus on modelling and studies regarding debris flow [ 245 , 246 , 247 , 248 , 249 , 250 ].

Cluster 4 (yellow color) is distant from the rest of the clusters, located at the extreme right of Figure 7 . This cluster comprises 37 authors, such as Masson D.G. (79 co-citations) and his studies in the underwater landslides are influenced by groundwater [ 251 , 252 , 253 ]. Grilli S.T. (49) and Hager W.H. (46) focus on the generation of modelling and numerical simulations linked to the movement of underwater masses and subsequent tsunamis [ 254 , 255 , 256 ].

Cluster 5 (purple color) is in the central part of the structure and has 32 authors, such as Hungr O. (259), who researches runout analysis and the generation of models for risk assessment [ 257 , 258 , 259 ]. Iverson R.M. (248) and Reid M.E. (77) focused on the study of debris flow and hydrological factors such as groundwater hydraulics [ 260 , 261 , 262 ].

Cluster 6 (light blue color) has 23 authors, such as Takahashi T. (73), Rickenmann D. (61) and Sidle R.C. (61), where the topics of interest highlight the study and analysis of debris flow [ 263 , 264 , 265 ].

Journal Co-Citation Analysis (JCA)

This analysis considers the relevance and similarity of journals in a field of study to reveal the intellectual structure [ 225 , 266 ]. JCA studies the number of times two journals are co-cited by another journal, revealing the various research fields that make up the intellectual structure [ 67 , 267 ].

Figure 8 shows this co-citation network of journals. The VOSviewer software version 1.6.17 is used to construct and visualize the connections between the various journals represented by nodes. This network shows 69 journals with at least 20 co-citations displayed in four clusters.

Visualization of the co-citation network assigning a representative color for each cluster (topics) and nodes (journals). According to the structure built using the VOSviewer software version 1.6.17. The colors red, green, blue and yellow appear in order of importance.

Cluster 1 of red color consists of 20 journals with 1239 citations, in which the following stand out: “Journal of Geophysical Research” in the category of Agricultural and Biological Sciences, Earth and Planetary Sciences, and Environmental Science; the “Journal of Fluid Mechanics” in Physics and Astronomy; and the “Journal of Hydraulic Engineering” in Environmental Science. The latter converge in the category of Engineering.

Cluster 2 (green color) contains 20 journals and 3526 citations, focusing mainly on the category of Earth and Planetary Sciences, such as the journals of: “Engineering Geology”, “Geomorphology” and “Landslides”.

Cluster 3 (blue color) focuses on the Earth and Planetary Sciences category and consists of 17 journals with 622 citations such as: “Marine Geology”, “Geological Society of America Bulletin” and “Geology”.

Cluster 4 (yellow color) has 12 journals and 834 citations, such as “Canadian Geotechnical Journal”, in the Engineering category, and “Environmental and Engineering Geoscience”, which have a focus on Environmental Sciences. These are intertwined with the “Geotechnique” journal in the Earth and Planetary Sciences category, reflecting the interconnection with the other clusters in Figure 8 .

4. Discussion

This study shows a consistent increase in scientific research on a landslide, thanks to the contribution of 64 countries spread over five continents ( Figure 5 ), in 15 languages, mostly in scientific articles and in the English language.

During the 90s, scientific production entered an introductory period, where Iverson R.M., Crosta G., and other authors contributed to the scientific community with the results of their analyses and studies (theoretical, laboratory and field) on the dynamic behavior of debris flows and landslides [ 101 , 105 ]. According to the Scopus database, this scientific production has experienced considerable growth since 2001 (representing 90.2% of publications).

In the decade 2001–2010, scientific research increased ( Figure 3 ), prioritizing the update of old studies such as the global rainfall threshold [ 113 ], the classification of landslides [ 109 ] and the generation of models [ 117 , 119 ], which in this period are essential for understanding and preventing landslides. Over the last decade (2011–2020), the increase in its scientific production has been stable, improving the development and combination of models generated in the previous period [ 125 , 126 ]. In this way, the analysis of landslides and the dynamic behavior of the debris flow, shallow landslides and their movement as a flow was perfected ( Figure 6 ).

The analysis of the intellectual structure of this field of study is conducted through three scientific maps:

In the analysis of co-occurrence of authors keywords, the application of geographic information systems (gis) and numerical simulations are a means for the study and analysis of landslides, debris flow and flow-like landslides, e.g., [ 184 , 213 ]. The sph (smoothed particle hydrodynamic) method is also part of this type of analysis, in conjunction with implementing sector rheology, e.g., [ 149 ]. Numerical models are the most common method for analyzing the main issues in each cluster, focusing on modelling, erosion, slope stabilization and rainfall among others, for such study, e.g., [ 174 ].

Secondly, the author co-citation analysis allows an observation of the interconnections that the various authors have in the entire landslide field ( Figure 7 ), which has international collaboration mainly from countries in Asia, Europe and North America ( Figure 5 ). One of the main topics of study is the shallow landslides, which since 1988 has focused on the analysis of propagation and transformation in debris flows [ 268 ]. This issue is related to the duration and intensity of rainfall analyzed by Guzzetti, et al., (2008) [ 113 ]. The authors characteristic of this analysis, such as Sassa (green cluster), Hungr (purple cluster), Takahashi (sky cluster), Guzzetti (red cluster), among others ( Figure 7 ), focus on the main hydrological and hydraulic, seismic and geomechanical factors causing the shallow landslide, debris flow, and consequently, the development of numerical models for risk prevention and assessment [ 229 , 232 , 234 , 235 , 238 , 241 , 264 , 265 , 269 ]. These topics are related to the red and blue clusters in Figure 6 .

In addition, the existence of small groups that are isolated from those previously mentioned is observed, which we detail below: (a) the group of Pastor, Cascini and Evans (blue cluster, Figure 7 ), they analyzed issues related to landslide dams, erosion, the susceptibility and stabilization of slopes referring to debris flows (blue cluster, Figure 6 ) [ 244 , 250 ], which is done through simulations [ 243 , 245 ] and mathematical models (e.g., smoothed-particle hydrodynamics—SHP [ 119 , 245 ]). (b) Masson, Grilli and Hager’s group (yellow cluster, Figure 6 ) study the action of groundwater and its influence on mass movement (underwater and on the surface), which can trigger the generation of tsunamis or the propagation of landslides such as flows, which can be analyzed using models and numerical simulations [ 251 , 254 , 255 , 256 ]. These topics are closely related to the green and yellow clusters ( Figure 6 ).

Third, in the journal co-citation analysis ( Figure 8 ), the red cluster is observed with a broad domain about the rest of the clusters in the categories of: Engineering, Agricultural and Biological Sciences, Physics and Astronomy, Earth and Planetary Sciences, and Environmental Science. Another field of study is that of Earth and Planetary Sciences (green and blue cluster, Figure 8 ), focusing on the hydraulic and geotechnical properties of the material and its formation environment (geological and geomorphological) [ 270 , 271 , 272 ]. The green and blue clusters are intertwined with the yellow cluster (Earth and Planetary Sciences, Figure 8 ), focusing on understanding landslides, improving the models in the assessment, and their classification [ 273 , 274 , 275 ]. Instead, given the diversity of the landslide science representing the red cluster ( Figure 8 ), it focuses on the behavior of the landslide, similar to that of a flow and the engineering analysis of the mechanical and hydraulic characteristics of the material [ 276 , 277 , 278 , 279 , 280 ]. This study is related to the group of authors Masson, Grilli and Hager (yellow cluster, Figure 7 ).

In this way, the entire intellectual structure and its topics of interest are analyzed, such as shallow landslide, debris flow, landslide and flow like landslide ( Figure 4 ), which cover the five classifications made by the USGS (fall, topple, slide, spread, and flow) ( Figure 1 ) [ 36 ].

5. Conclusions

This work analyses the scientific production of the research field of landslides, according to the classification addressed by the USGS. It allows an exploration and analysis of the intellectual structure of 632 publications from the Scopus database, which is feasible for a bibliometric study. When performing the performance analysis, its constant evolution is visualized between 1952–2020 ( Figure 3 ), with a significant increase in the last 20 years. The 74% corresponds to scientific articles ( Figure 4 ), the majority of which are in English. The scientific contribution is concentrated in 64 countries, led by China ( Figure 5 ).

The debris flow is a type of landslide generated by various causes, such as precipitation and collapse of landslide dams. This field of study analyzes the material’s hydraulics, geodynamics and geological properties in the face of hydrometeorological and seismic events, which are an essential part of the propagation of landslides with a flow behavior and subsequent generation of debris flow ( Figure 6 ). Some authors present studies related to the subject, such as Guzzetti F., Crosta G.B., Godt J.W., Sassa K. and Wang G., among others (see Figure 7 ).

The shallow landslide is an area of study supported since 1980 by Nel Caine and by Guzzetti et al., 2008, who analyze this type of landslides as a consequence of the duration and intensity of rains. This research area is in a period of growth. Therefore, it links the material’s hydrological processes and hydraulic conditions as its main triggering factors. Therefore, the implementation of numerical models for slope stabilization and risk prevention enhances their importance ( Figure 6 ). In addition, the group of co-cited authors, such as Guzzetti, Crosta and Godt (red cluster, Figure 7 ), analyze a large part of these landslides, which may be the basis for understanding debris flow formation and other types of landslide.

It is essential to mention that the intellectual structure of this research field made it possible to point out or list topics of interest that can increase scientific knowledge of this subject, such as:

• The analysis of the hydraulic properties and the circumstances by which landslides can be generated as a flow;
• a deeper analysis in the study of shallow landslides and their propagation in debris flow and flow-like landslides;
• analysis of landslides from the point of view of rheology, focusing on the movement of materials caused by earthquakes and rainfalls, among others;
• generation of models through the Smoothed-Particle Hydrodynamics (SPH) method, which has been widely used for cases such as debris flow, shallow landslides, and other types of mass movements such as flows;
• implementation of satellite images in the areas of the different landslides, where the most widely implemented methods are: Interferometric Synthetic Aperture Radar (InSAR), Unmanned Aerial Vehicle (UAV), and Geographic Information System (GIS);
• stabilization studies in landslide dams, which can be caused by rainfalls and subsequent generation of debris flow;
• a technical and geological analysis on topics related to submarine landslides, among which run-out analysis and the propagation of tsunamis due to landslides and earthquakes stand out, this being an area of study that is evolving.

We consider that this study is a contribution to the academic literature due to: (i) The possibility of getting to know different researchers in specific topics of this field of study, which allows the establishment of collaboration networks; (ii) to know the experiences validated by the different authors, using techniques and methods of study that enrich scientific knowledge; and (iii) the study serves as a guide for novice researchers who wish to know in brief outlines this general structure of knowledge.

Finally, there are some limitations to this work: (a) restriction due to the classification of landslides, only to the contribution of the USGS; and (b) the use of the database (Scopus), without considering other existing bases in the academic world such as the Web of Science or Dimensions. Considering these limitations, future research is estimated using different databases and other classifications related to landslides.

Acknowledgments

This research study was possible with the valuable contribution of the “Registry of geological and mining heritage and its impact on the defence and preservation of geodiversity in Ecuador” academic research project by ESPOL University under grant nos. CIPAT-01-2018, the support of NOVA Science Research Associates and Geo-resources and Applications GIGA, ESPOL.

Author Contributions

Conceptualization, P.C.-M., N.M.-B., A.Q.-R., F.M.-C. and B.A.-M.; methodology, P.C.-M., N.M.-B., F.M.-C. and B.A.-M.; software, N.M.-B. and B.A.-M.; validation, N.M.-B. and B.A.-M.; formal analysis, P.C.-M., N.M.-B., A.Q.-R., F.M.-C. and B.A.-M.; investigation, P.C.-M., N.M.-B., A.Q.-R., F.M.-C. and B.A.-M.; data curation, N.M.-B. and B.A.-M.; writing—original draft preparation, P.C.-M., N.M.-B., A.Q.-R., F.M.-C. and B.A.-M.; writing—review and editing, P.C.-M., N.M.-B., A.Q.-R., F.M.-C. and B.A.-M.; visualization, N.M.-B. and B.A.-M.; supervision, P.C.-M., N.M.-B. and F.M.-C. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• Technical Review
• Published: 10 January 2023

Landslide detection, monitoring and prediction with remote-sensing techniques

• Nicola Casagli 1 ,
• Emanuele Intrieri   ORCID: orcid.org/0000-0002-9227-4409 1 ,
• Veronica Tofani 1 ,
• Giovanni Gigli 1 &
• Federico Raspini 1  

Nature Reviews Earth & Environment volume  4 ,  pages 51–64 ( 2023 ) Cite this article

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• Geomorphology
• Hydrogeology
• Natural hazards

Landslides are widespread occurrences that can become catastrophic when they occur near settlements and infrastructure. Detection, monitoring and prediction are fundamental to managing landslide risks and often rely on remote-sensing techniques (RSTs) that include the observation of Earth from space, laser scanning and ground-based interferometry. In this Technical Review, we describe the use of RSTs in landslide analysis and management. Satellite RSTs are used to detect and measure landslide displacement, providing a synoptic view over various spatiotemporal scales. Ground-based sensors (including ground-based interferometric radar, Doppler radar and lidar) monitor smaller areas, but combine accuracy, high acquisition frequency and configuration flexibility, and are therefore increasingly used in real-time monitoring and early warning of landslides. Each RST has advantages and limitations that depend on the application (detection, monitoring or prediction), the size of the area of concern, the type of landslide, deformation pattern and risks posed by landslide. The integration of various technologies is, therefore, often best. More effective landslide risk management requires greater leveraging of big data, more strategic use of monitoring resources and better communication with residents of landslide-prone areas.

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Casagli, N., Intrieri, E., Tofani, V. et al. Landslide detection, monitoring and prediction with remote-sensing techniques. Nat Rev Earth Environ 4 , 51–64 (2023). https://doi.org/10.1038/s43017-022-00373-x

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GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia

• Tilahun Mersha 1 , 2 &
• Matebie Meten 1  

Geoenvironmental Disasters volume  7 , Article number:  20 ( 2020 ) Cite this article

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Simada area is found in the South Gondar Zone of Amhara National Regional State and it is 780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of Ethiopia. This area is part of the Guna Mountain which is characterized by weathered volcanic rocks, rugged morphology with deeply incised gorges, heavy rainfall and active surface processes. Many landslides have occurred on August 2018 after a period of heavy rainfall and they caused many damages to the local people. In this study, Frequency Ratio (FR) and Weights of Evidence (WoE) models were applied to evaluate the landslide causative factors and generate landslide susceptibility maps (LSMs). The landslide inventory map that consists of 576 active and passive landslide scarps was prepared from intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors including aspect, slope, curvature, lithology, land use, rainfall and distance to stream were combined with a training dataset using GIS tools to generate the LSMs of the study area. Then the area was divided into five landslide susceptibility zones of very low, low, moderate, high and very high. Later, the resulting maps have been validated by using area under the curve and landslide density index methods. The result showed that the predictive rate of FR and WoE models were 88.2% and 84.8%, respectively. This indicated that the LSM produced by FR model showed a better performance than that of WoE model. Finally, the LSMs produced by FR and WoE models can be used by decision-makers for land use planning and landslide mitigation purpose.

Introduction

Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas which caused a significant injury and loss of human life, damage in properties and infrastructures (Parise and Jibson 2000 ; Dai et al. 2002 ; Glade et al. 2005 ; Kanungo et al. 2006 ; Pan et al. 2008 ; Girma et al. 2015 ). The term “landslide” is the movement of a mass of rock, debris or earth down a slope under the influence of gravity (Varnes 1978 ; Hutchinson 1989 ; WP/WLI - International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory 1990 ; Cruden 1991 ; Cruden and Varnes 1996 ). Landslides are caused by different triggering factors such as heavy or prolonged precipitation, earthquakes, rapid snow melting and a variety of anthropogenic activities. Landslides can involve flowing, sliding, toppling or falling movements and many landslides exhibit a combination of two or more types of movements (Crozier 1986 ; Cruden and Varnes 1996 ; Dikau et al. 1996 ).

Landslide in Ethiopia is a common phenomenon which often causes significant damage to people and property. Almost 60% of the total population in Ethiopia lives in the highland areas (Ayalew 1999 ) which is characterized by high relief, complex geology, high rainfall, rugged morphology, very deep valleys and gorges with active river incision. The rapid population growth demanded the use of areas which were not previously used for settlement, urban expansion, agricultural and other purposes thereby exposing these areas to landslide problems after rainy seasons (Temesgen et al. 2001 ; Abebe et al. 2010 ; Woldearegay 2013 ).

In recent years landslide incidences are increasing in the Ethiopian highlands due to man-made and natural causes (Meten et al. 2015b ). For instance, from 1960 to 2010 alone, Landslides have killed 388 people, injured 24 people, and damaged agricultural lands, houses and infrastructures (Ayalew 1999 ; Temesgen et al. 1999 ; Woldearegay 2008 and (Ibrahim: Landslide assessment and hazard zonation in Mersa and Wurgessa, North Wollo, Ethiopia, unpublished)). According to Abebe et al. ( 2010 ), the highlands and mountainous area of Ethiopia like the Blue Nile Gorge, the Lower Wabe-Shebele River valley, Gilgel Gibe River, Tarmaber, Kombolcha - Dessie road, Uba Dema village in Sawla, Wondogenet area and many other parts of Ethiopia are repeatedly facing problems associated with landslides. The landslides in these areas are affecting human lives, infrastructures, agricultural lands and the natural environment. As a result of this, the study of the landslide has drawn global attention to increase awareness about its socioeconomic impacts and the pressure of increasing population and urbanization on mountainous areas (Kanungo et al. 2006 ).

The current study area is found in Simada District of South Gondar Zone in the Amhara National Regional State of Northwestern Ethiopia. It is part of the northwestern Ethiopian highlands. This area is severely affected by landslide incidences in recent years. Landslide incidence in the study area occurred on August, 2018 after a heavy and prolonged rainfall that caused the death of animals, destruction of houses and wide areas of cultivated and non-cultivated lands. Therefore, this area requires a detailed investigation to evaluate the causes, types and failure mechanisms of landslides and to prepare the landslide susceptibility maps. A systematic landslide study helps to reduce the damages in infrastructures, houses and cultivated lands and loss of lives. This importance will be noticed when these landslide susceptibility maps are used by decision-makers in regional land use planning, landslide prevention and mitigation measures.

For proper and strategic land use planning, it is important to evaluate and delineate landslide prone areas using different landslide susceptibility mapping techniques. Preparing a landslide susceptibility map of a certain area is a useful tool in landslide hazard management as it shows the degree of susceptibility of an area to landslide occurrence. It is obvious that landslide susceptibility maps can be generated based on the assumption that future landslide will occur under the same condition as in the past (Pham et al. 2015 ). Interpretation of future landslide occurrence needs an understanding of conditions and processes that control landslides in the study area. Past landslides and different conditioning factors such as slope morphology, hydrogeology and geology of the area are the main parameters to assess and evaluate landslide susceptibility by integrating these conditioning factors and past landslides in a GIS environment.

GIS-based landslide susceptibility mapping techniques have been used by several researchers (Aleotti and Chowdhury 1999 ; Kanungo et al. 2009 ) which can be classified into qualitative and quantitative ones (Yalcin et al. 2011 ; Felicisimo et al. 2012 ; Peng et al. 2014 ; Wang and Li 2017 ). Qualitative techniques include geomorphological analyses and inventory methods. These are based on expert judgment and are more subjective than quantitative methods. Quantitative methods such as deterministic analyses, probabilistic approaches and statistical techniques closely rely on mathematical models which have much less personal bias but still needs experience to produce and run these models (Aleotti and Chowdhury 1999 ; Kanungo et al. 2009 ). In recent years, many landslide susceptibility maps were produced using GIS-based statistical approaches like Frequency Ratio (FR) and Weights of Evidence (WoE) models. This is because the result from these models showed good performance with high accuracy and these models are very simple to implement and can provide the contribution of each causative factor class for landslide occurrence (Lee and Pradhan 2007 ; Akgun et al. 2007 ; Dahal et al. 2008 ; Işık Yilmaz 2009 ; Pradhan, Lee and Buchroithner 2010 ; Choi et al. 2012 ; Park et al. 2012 ; Vakhshoori and Zare 2016 ; Fayez et al. 2018 ).

Several researchers have used Frequency ratio model on landslide studies (Bahrain et al. 2014 ; Meten et al. 2015a ; Haoyuan Hong et al. 2015 ; Pham et al. 2015 ; Pirasteh and Li 2017 ; Fayez et al. 2018 ; Khan et al. 2019 ) and in comparison with a few methods (Akgun et al. 2007 ; Lee and Pradhan 2007 ; Işık Yilmaz 2009 ; Choi et al. 2012 ; Park et al. 2012 ; Meten et al. 2015b ; Wang and Li 2017 ). A combination of both FR and WoE models have been applied for landslide susceptibility mapping (Regmi et al. 2013 ; Rahmati et al. 2016 ). Gholami et al. ( 2019 ) also compared the prediction capability of frequency ratio, fuzzy gamma and landslide index models. Each GIS-based statistical method requires data on past landslides, preparatory causative factors and triggering factors. To prevent or mitigate any damage from landslides, it is essential to assess the landslide prone areas. The current study aims to carryout landslide susceptibility mapping by applying FR and WoE models in order to highlight critically high and very high hazard zones. This will help to reduce and mitigate any hazard associated with future landslide occurrence.

The study area is 185.7 square kilometers which is located in Simada District of South Gondar Zone, Amhara National Regional State, Ethiopia (Fig. 1 ). The area is bounded between 38 0 11' E and 38 0 20' E longitudes and 11 0 30' N and 11 0 41' N latitudes. The typical drainage pattern of the study area is dendritic and parallel. Atkus and Kostet Rivers are the main rivers that affect the study area by eroding the banks of rivers leading to slope instability. The confluence of these rivers forms Bijena River which is the largest river in the study area. Most of the rivers in the study area flow towards the southeast direction. The physiography of the study area forms the rugged topography of Guna Mountain (Fig. 2 ) which is part of the northwestern Ethiopian highlands. The area can be classified into two main physiographic regions. These are the plateau area and the rugged terrain. The plateau areas are characterized by volcanic landscapes that represent the high flatlands of the Kefoye, Agona and Jinjero Gedel areas. These areas are water divide zones in which rivers are flowing to Abay Basin in the west and to Bashilo Basin in the south. In this area, the slopes are ranging from flat slopes on the top to steeper slopes at the plateau scarp. The rugged terrain is highly dissected by major rivers and streams which are characterized by deep narrow valleys and gorges. Slopes in these areas are steep to vertical and susceptible to erosional and landslide phenomena. The elevation of the study area ranges from 2067m to 3586 m which comprises of medium to very high relief hills. The presence of steep scarps, rugged slope faces, deep gorges and steep ridges showed that this area is prone to active surface processes and landslide incidences. Based on elevation, the climatic zones of the study area are mostly falling under the highland climatic zone. The primary wet season extends from June to September. There is great variation in the rainfall amounts with maximum rainfall occurring during the wet season which starts in June and ends in September with the heaviest rainfall occurring during the months of July and August.

Location map of the study area

Three dimensional map of the study area

In order to achieve the objectives of this research, data collection and organization, preparation of landslide inventory datasets, database construction of landslide causative factors and application of FR and WoE models were carried out to prepare the landslide susceptibility maps and validate them.

Data collection and organization

The necessary data for this study were collected from various sources. These include collecting relevant literatures from published and unpublished papers, DEM data from USGS, a regional geological map from Geological Survey of Ethiopia at a scale of 1:250000, rainfall data from National Metrological Agency of Ethiopia, a topographic map from Ethiopian Geospatial Information Agency at a scale of 1:50000 and Google Earth image from Google Earth. During field work, data collection was carried out on different rock types by describing their character, the relative degree of weathering, slope steepness, location of springs and swamps, landslide inventory mapping on both active landslide and scarp areas by measuring their length, width, accumulation zone and depth (if possible), land use and land cover, man-made activities including farming practice. After compilation of the actual field investigation, the data has been systematically processed and analyzed first in ArcGIS followed by Microsoft Excel and then finally in ArcGIS.

Preparation of landslide inventory dataset

The quality of the landslide inventories depends on the accuracy, type and certainty of the information shown in the maps. New and emerging mapping methods, based chiefly on satellite, aerial and terrestrial remote sensing technologies, can greatly facilitate the production and the update of landslide maps. Literature review has shown that the most promising approaches exploit VHR optical, monoscopic and stereoscopic satellite images, analyzed visually or through semi-automatic procedures, and VHR digital representations of surface topography captured by LiDAR sensors. A combination of satellite, aerial and terrestrial remote sensing data represents the optimal solution for landslide detection and mapping, in different physiographic, climatic and land cover conditions (Guzzetti et al. 2012 ). Ye et al. ( 2019 ) detected landslides from hyperspectral remote sensing data using a deep learning technique.

The landslide inventory dataset in the current study consist a total of 576 landslides which were identified from Google Earth image interpretation and intensive field survey. For landslide susceptibility mapping landslide polygons can be divided into training and validation datasets. The training dataset is used for constructing the predictive model while the validation dataset is used for validating the model. In this study, the specific date of landslide occurrence is not well known. Hence, the landslide polygons were randomly split into two classes with 80% for training and 20% for validation by keeping their spatial distribution into account (Fig. 3 and 4 ). In addition, the validation data sets for most of the landslide susceptibility or hazard assessments were chosen in between 20% and 30% of the total landslide inventory.

Flowchart of the research work (Note: LS = landslide)

Landslide inventory map showing the distribution of landslides

Database for landslide causative factors

To undertake landslide susceptibility analysis in the study area, a spatial database was first constructed for the causative factors within the spatial analysis tools of ArcGIS 10.4 software. The database consists of the landslide inventory datasets (training and validation) and the landslide causative factors (slope, aspect, curvature; land use, lithology, rainfall and distance from stream). These factors were subsequently evaluated by calculating their weights from the relationship between the landslide and landslide causative factors and then these results were verified. There are no strict rules or guidelines for the triggering factors to be used in different statistical approaches for landslide susceptibility mapping. Instead, the chosen factors should be operative and measurable depending on a particular area’s characteristics (Ayalew and Yamagishi 2005 ). One parameter may be an important controlling factor for landslide occurrence in a certain area but in most cases a combination of two or more landslide causative factors may be effective in addition to the triggering factor for landslide occurrence.

In this study, the triggering factor was heavy and prolonged rainfall. During the fieldwork, landslide locations were identified and marked with GPS, land use (land cover) types around the landslide scar, drainage networks and spring locations, lithological units and human activities were investigated to prepare the landslide susceptibility maps.

Generally, the selection of landslide causative factors should consider the nature of the study area and the availability of data. In this regard, a total of seven parameters were selected including slope, aspect, curvature, lithology, rainfall, land use and distance to stream. All causative factor maps were converted into raster maps with the same coordinate system (WGS 1984 UTM zone 37N) and the same pixel size (30mx30m). The rasterized training (80%) landslide map and all the causative factor maps have been overlaid and the information was extracted using the spatial analyst tool of ArcGIS to calculate the ratings or weights of all factor classes for FR and WoE models. The summation of these ratings or weights of each landslide factor will help to evaluate the spatial relationship between them and the probability of landslide occurrence in the study area.

Topographic parameters like slope, aspect, curvature and distance to stream maps were derived from Digital Elevation Model (DEM) with a cell size of 30 m by 30 m. Lithology and land use maps were prepared from intensive fieldwork and Google Earth image interpretations. The rainfall map was generated using IDW interpolation technique of the spatial analyst tool in ArcGIS from four rain gage stations near to the study area using the rainfall data from National Meteorological Agency of Ethiopia.

Frequency ratio (FR) model

Frequency Ratio model is a well-known and widely used bivariate statistical method that is used for landslide susceptibility mapping (Lee and Talib 2005 ; Akgun 2012 ; Demir et al. 2013 ; Mezughi et al. 2011 ; Yalcin et al. 2011 ; Abay and Barbieri 2012 ; Mondal and Maiti 2013 ; Paulin et al. 2014 ). In this model, processing the input data, computations and output-processes are very simple and can be easily understood. It is simple and relatively flexible to use and implement a landslide susceptibility map with accurate results (Lee and Pradhan 2007 ; Yilmaz 2009 ; Choi et al. 2012 ; Mohammady et al. 2012 ; Park et al. 2012 ). The frequency ratio model is one of the probabilistic models which are based on the observed relationship between the distribution of landslides and each landslide related factor (Lee and Talib 2005 ). To evaluate the contribution of each factor towards landslide susceptibility, the training landslide group was combined with thematic data layers separately and then the frequency ratio of each factor’s class was calculated according to the following procedures.

First, the number of pixels for landslide occurrence and non-occurrence in each factor’s class was calculated. Second, the percentage of each factor’s class having landslide to the total pixels containing landslide of the factor was calculated and the percentage of each factor class’s number of pixels to the total number of pixels in the study area was calculated. Finally, the frequency ratio of each factor class was obtained by dividing the percentage of landslide pixels to the percentage of area pixels in each factor classes (Equation 1 ).

Where; Npix ( S i , j ) = the number of pixels containing landslide within class j in factor i; Npix ( N i , j ) = the number of pixels of class j in factor i; ∑ j NPix ( S i , j ) is the number of total pixels containing landslide in the study area; ∑ j NPix ( N i , j ) is the number of total pixels in the study area.

The calculated FR value represents the degree of correlation between landslide and a certain class of the causative factor. A value of 1 is an average value for the landslide occurrence of a specific landslide causative factor class. A value more than 1 indicates a strong and positive correlation and a high probability of landslide occurrence, while a value of less than 1 indicates a negative relationship and low probability of landslide occurrence in a certain class of a landslide causative factor. The FR map of each causative factor is prepared with the help of ArcGIS by assigning the calculated FR values. Then the FR values of all the causative factor maps were overlaid and numerically added using the raster calculator of the spatial analyst tool in ArcGIS 10.4 to prepare the Landslide Susceptibility Index (LSI) map. LSI is computed by summing the FR values of all the landslide causative factor maps (Equation 2 ) and then the resulting LSI map was further reclassified in to very low, low, moderate, high and very high landslide susceptibility classes.

Where: LSI = Landslide susceptibility index, FR is the frequency ratio and n is the number of selected causative factors. The calculated values of FR for each pixel in the LSI indicate the relative susceptibility to landslide occurrence. The higher LSI pixel values have high susceptibility to landslide occurrence while the lower LSI pixel values have lower susceptibility (Akgun et al. 2007 ).

Weights of evidence (WoE) model

WoE model is a log-linear form of the Bayesian probability model for landslide susceptibility assessment that uses landslide occurrence as a training point to drive prediction outputs. It calculates both unconditional and conditional probability of landslide hazards. This method is based on the calculation of positive and negative weights to define the degree of spatial association between landslide occurrence and each explanatory variable class (Pardeshi et al. 2013 ). The positive weights (W+) indicate the occurrence of an event while the negative weight (W-) indicates the non-occurrence of an event. To evaluate W + and W - , calculating the following parameters is important.

Nmap = total number of pixels in the map

Nslide = number of pixels with landslides in the class

Nclass = number of pixels in the class

NSLclass = number of pixels with landslides in the class

The values needed for the weight of evidence formula are:

Npix1 = NSLclass

Npix2 = Nslide – NSLclass

Npix3 = Nclass – NSLclass

Npix4 = Nmap – Nslide – Nclass + NSLclass

Then the positive and negative weights are calculated as follows (Equations 3 and 4 ).

Where Npix 1 is the number of landslide pixels present on a given factor class, Npix 2 is the number of landslides pixels not present in a given factor class, Npix 3 is the number of pixels in a given factor class in which no landslide pixels are present and Npix 4 is the number of pixels in which neither landslide nor the given factor is present (Van Westen 2002 ; Dahal et al. 2008 ; Regmi et al. 2010 ). These weights are used to calculate a weight of contrast value (C) for the particular susceptibility variable (Equation 5 ).

The contrast value (C) measures the strength of a relationship between the causative factors and landslides. If the contrast value is positive, it will have a positive spatial association while the negative one will have a negative spatial association. The weighted map ( Wmap ) for each landslide causative factor can be prepared by summing the weights of contrast(C) values of each factor class. Similarly, the final landslide susceptibility index (LSI) map was prepared by summing all the weighted maps (∑ Wmap )  of each landslide causative factor through a raster calculator of map algebra in the spatial analyst tool of ArcGIS as follows (Equations 6 and 7 ).

Landslide inventory

During August, 2018, an intense rainfall in Simada area triggered many landslides that occurred mostly in rural areas. The damage was severe in the villages of Dubdubiya, Asfa Meda, Gedeba, Ditorka and at several other sites along the river courses. Particularly, in Dubdubiya and Asfa Meda villages, landslides damaged 81 dwellings, killed 14 goats, affected thousands of people, damaged hundreds of hectares of farmlands and dislocated 486 people. These problems occurred in these villages as the settlement areas are mostly located at the foot of a steep slope that is covered by weathered volcanic rocks as well as the presence of stream accumulated debris and earth flows that can suddenly burst out at the at the outlets of a mountain. Landslide inventory map of the study area (Fig. 4 ) was prepared from the combination of an intensive field survey and Google Earth image interpretations. Extensive field studies conducted from mid-November to mid-December of 2018 helped us to map known landslides using GPS and check the size and shape of these landslides in order to identify the type of movements, materials involved and to determine the state and activity of landslides (active, reactivated, dormant, etc.). This inventory data was mapped as vector-based polygon data and then converted to the raster format with a pixel size of 30m by 30m in ArcGIS 10.4.

In the present study area, a total of 576 landslides that contain 6304 pixels were identified and divided randomly into training and validation landslides by keeping their spatial distributions into account. The training landslides that accounted 80% of landslides with 5126 pixels were used for building the predictive model while the validation landslides that accounted 20% of landslides with 1178 pixels were used for validating purpose. From the total landslide polygons, 117 landslides were active landslides collected from field investigations while the remaining 459 landslide polygons were collected from time serious Google Earth image interpretations.

Landslide locations are predominantly distributed in the south-central, in the north and in the eastern parts of the study area with decreasing order of landslide density, damage on agricultural land and infrastructures. This area consists of a rugged and mountainous terrain which is characterized by steep slopes, deep gorges, high relief and fractured and weathered rocks. The common types of landslide occurrence in the study area include rock slide, rockfall, earth slide, debris slide and debris flow, rotational and translational soil slide, translational debris slide, rotational debris slide and complex types of slides. Generally, these landslides predominantly affected the rural areas in which the type of landslides and their probable causes and damages are described below.

Most prominent landslides occurred in Asfa Meda, Dubdubiya, Tej Wuha-Gedeba and Ditorka-Megersum Villages. Landslides in Asfa Meda Village occurred at the interface between thin residual soils and rhyolitic rock and most of the landslides are shallow rotational and/or translational earth slides. Most of Dubdubiya village was highly affected by stream undercutting, erosion of the slope surface, riverbank erosion and improper farming practice (Fig. 5 ). The slope materials are dominantly covered by weathered basalt and colluvial deposits. Erosional opening surfaces and tension cracks were observed during field investigation indicating that seeping water might have brought instability of the slope through internal erosion of the weathered materials. A typical example of a landslide in this village was the landslide that occurred near Arata Gabriel Church. The main causes of this landslide were stream/river undercutting, presence of spring on top of the slope and colluvial soil slope materials. The slope material in Teji Wuha and Gedeba Villages is dominantly covered with weathered tuff and thin residual soils. In this village, there is an indication of shallow groundwater since the swamp area and many springs are observed with rotational and soil creep. Creeping of soil was identified by tilting of powerlines and fences (Fig. 6 d). The common types of landslides that were observed in Ditorka and Megersum villages were rockslide (Fig. 6 a), rock fall, debris slide (Fig. 6 b) and rotational slide.

Historical landslide data in Dubdubiya Village as detected from Google Earth image. The white arrow and the red broken line in the figures indicate the direction of the slope movement and a landslide boundary respectively. a Image before the landslide event. b Image after landslide occurrence and, c Photo of the current condition

Field photo of landslide and their damage in different villages of the study area (Red and blue colored arrow indicates the direction of slope movements and River/stream flows respectively). In Ditorka and Megersum Villages- ( a ) Rockslide ( b ) Debris slide; Teji Wuha and Gedeba Villages- ( c ) Earth slide on the gravel road (damaged culvert) ( d ) Creep (tilting of powerline); in Asfa Meda Village- ( e ) and ( f ) Complex type landslides; in Dubdubiya Village- ( g ) Shallow debris flow that damaged wheat crop and ( h ) Rockslide

Landslide causative factors

The spatial distribution and density of landslides are mainly controlled by topography of an area, weather condition, geology, land use/land cover and anthropogenic factors (Khan et al. 2019 ). Consequently, evaluating the impact of these causative factors on the spatial distribution of landslides is very important in order to understand their failure mechanism and to prepare the landslide susceptibility map. In this study, seven causative factors that have been used for the preparation of landslide susceptibility maps include slope, aspect, curvature, lithology, land use/ land cover, rainfall and distance to stream. The roles played by each of these causative factors will be discussed in the following sections.

Slope is a very important parameter for landslide study as it has a direct relation with landslide occurrence. As a result, it is frequently used in preparing a landslide susceptibility map (Yalcin and Bulut 2007 ). It is well known that landslide occurs more frequently on steeper slopes due to gravity stress. The slope map (Fig. 7 a) of the study area was prepared from DEM data. It was divided into five classes such of 0 – 5 0 , 5 0 – 12 0 , 12 0 – 30 0 , 30 0 – 45 0 , and > 45 0 . For slope classes above 12 0 , the frequency ratio is increasing which indicate the higher probability of landslide occurrence in these classes (Table 1 ).

Landslide causative factor maps ( a ) Slope, ( b ) Curvature, ( c ) Distance to stream ( d ) Aspect ( e ) Land use, ( f ) Lithology and ( g ) Rainfall

Curvature map of the study area was generated from DEM data and it was classified into 3 classes of concave, convex and flat surfaces (Fig. 7 b). Following heavy rainfall, a convex or concave slope contains more water and retains this water for a longer period (Lee and Talib 2005 ). The more positive or negative values indicate the higher probability of landslide occurrence. In the flat area, the probability of landslide occurrence is very low. A positive curvature indicates that the surface was upwardly convex at that grid. A negative curvature indicates that the surface was upwardly concave at that grid and a value of zero indicates that the surface is flat.

Aspect refers to the slope orientation which is generally expressed in terms of degree from 0 0 – 360 0 . It is considered as an important factor in landslide studies as it controls slope’s exposure to sunlight, wind direction, rainfall (degree of saturation) and discontinuity conditions (Komac 2006 ). Slope aspect map (Figure 8 c) in this study area was derived from DEM data and it was divided into nine classes, namely; north (0 – 22.5, 337.5 - 360,), northeast, east, southeast, south, southwest, west and northwest (Fig. 7 d).

Landslide Susceptibility Map of the study area using: a FR and b WoE Models

Distance to stream

The proximity of the slope to the stream course is an important factor that dictates the landscape evolution of the area and an indicator of the landslide and related erosional aspects. Rivers with a number of drainage networks have a high probability of landslide occurrence as they erode the slope base and saturate the underwater section of the slope forming material (Akgun and Turk 2011 ).

Since there are many streams in the study area which flow into Kostet, Atkus and Bijena Rivers, many landslides occurred in the close vicinity of these rivers. Hence, this parameter was considered as one causal factor in landslide susceptibility analysis. Zones with parallel pattern of drainage in steep slopes are the most probable landside sites. Drainage often plays its own role in developing pore-water pressure which reduces the shear strength of slope materials. Streamlines were derived from DEM data and it was classified based on stream order.

Landslide in this area is mostly associated with 1 st , 2 nd , and 3 rd order streams. Distance from stream map was developed from Euclidean distance buffering method in the spatial analyst tool of ArcGIS 10.4. This map was classified in to five subclasses: 0 – 50, 50 – 100, 100 – 150, 150 – 200 and > 200 meter (Fig. 7 c).

Land use / land cover

Land-use change has been recognized throughout the world as one of the most important factor influencing the occurrence of rainfall-triggered landslides. Changes in land use/cover resulted from man-made activities such as deforestation, overgrazing, intensive farming and cultivation on steep slope can initiate slope instability (Glade 2003 ). Vegetation has a major contribution to resist slope movements. Vegetation having a well-spread network of root systems increases shearing resistance of the slope material. This is due to the natural anchoring of slope materials. In addition to this, it reduces the action of erosion and adds the stability of the slope. In another way, barren or sparsely vegetated slopes are usually exposed to erosion and thus it has the effect of increasing slope instability. The land use map of the study area was prepared from the Google Earth image of 2016 and the analysis was done in ArcGIS. About seven land-use types were identified including moderate forest, sparse forest, bush, grazing land, agricultural land, settlement and river (Fig. 7 e). The area is predominantly covered by agricultural land and grazing land.

Lithology is one of the most controlling parameters in slope stability since each class of materials has different shear strength and permeability characteristics (Yalcin and Bulut 2007 ). Different rock types have varied composition and structure which contribute to the strength of the slope material in a positive or negative way. The stronger rock units give more resistance to the driving forces as compared to the softer/ weaker rocks. Lithological map of the study area was prepared from existing regional geological map (with a scale of 1:250,000) as a preliminary map for further improvement of a lithologic map into a scale of 1:50,000 based on a detailed field survey. The study area contains seven lithological units namely Trachyte, Weathered tuff, Rhyolite, Weathered basalt, Residual soils, Colluvial and Alluvial Deposits (Fig. 7 f).

Rainfall is considered as an influencing factor to cause slope instability. Precipitation, particularly intense and prolonged rains are controlling factors that trigger landslides by providing water thereby increasing underground hydrostatic level and pore water pressure. When the soil undergoes such pressure changes, water within it will create negative or upward pressure, as it cannot drain quickly. When the pore water pressure is equivalent to the upper pressure, the shearing resistance of the material decrease and will lead to failure of the material. The rainfall data of the four stations that surround the study area were collected from National Metrology Agency of Ethiopia. There are various interpolation techniques in ArcGIS to interpolate rainfall over a large area based on few point data. These include Thiessen polygon, Isohyetal, average arithmetic, inverse distance weight (IDW) and Kriging. The general assumption of the IDW method of interpolation is that the value of unsampled point is the weighted average of known values within the neighborhood. Therefore, the values from a scattered set of known points can be utilized to assign rainfall values to unknown points. It can be used to compute the unknown spatial rainfall data from the known sites that are adjacent to the unknown sites (Chen and Liu 2012 ). The rainfall map of the study area was prepared using the IDW interpolation method in GIS. The rainfall data analysis showed that the maximum monthly rainfall occurs in June, July, August and September which coincides with the landslide occurrence in this area. The rainfall map of the study area was divided into five annual rainfall classes of 627 – 727, 727 – 813, 813 – 901, 901 – 994 and 994 - 1125.2 millimeters (Fig. 7 g) by the natural breaks method.

Result and discussion

Relationship between landslide and causative factors.

This study has analyzed the relationship between seven causative factors and landslide occurrence. Using the FR and WoE models, the relative frequency values and the weights of values were calculated respectively. The causative factors were classified into different classes and weights were assigned to them for both FR and WoE models as presented in Table 1 and 2 respectively. These results showed that the relative susceptibility of each class is almost similar for both models but the parameters and results are different from each other. This implies that if a factor class has lower and higher values in both models, the susceptibility will also be lower and higher respectively. In case of FR model, the spatial relationship between the causative factors and landslide is determined by FR values. The causative factor classes with FR value > 1 will have a high degree of landslide occurrence. On the other hand, for the WoE model, C describes the correlation and spatial association of the landslide with the causative factors. The positive C values indicate a positive association with more landslide occurrence and vice versa for negative C values. The weights with higher values indicate a higher degree of influence on landslide occurrence. Generally, the factor class values derived from each model showed the spatial relationship of the causative factors in their contribution to landslide occurrence. The association is more or less the same in both models.

The slope classes > 12 0 have higher contribution for landslide occurrence. The area with a slope class > 45 0 is the most landslide prone class while the area with a slope class < 5 0 is the least one. Generally, as the slope increases, the probability of landslide occurrence also increases. In case of aspect classes, the FR values of slope classes facing towards the northeast (22.5 – 67.5), east (67.5 – 112.5) and north (0 – 22.5) are greater than one indicating a higher probability of landslide occurrence. The northeast facing aspect class has got the maximum weight or rating followed by the east facing ones. The curvature range of (-3.6) - (-0.001) has a greater contribution to the slope failures. In case of lithology, three units i.e. colluvial deposit, weathered basalt and rhyolite have high probability of landslide occurrence. Colluvial deposit and weathered basalts have less strength and hence susceptible to landslides. Rhyolitic rocks in the study area formed a cliff underlying thin residual soils. As a result, most of the landslides occurred at the contact between rhyolite and thin residual soils.

The type of land use also controls the occurrence of landslide in the study area. The highest weights or ratings were observed in the land use types of grazing land, river, sparse forest and bushes indicating a high probability of landslide occurrence. The highest weighted value of grazing land is due to its exposure to erosion and weathering. In case of the relationship between landslide occurrence and the distance from stream, as the distance from stream increases, the occurrence of landslide generally decreases. Landslide occurrence is higher in the first three classes of 0 – 50m, 50 – 100m and 100 – 150m (Table 1 and 2 ). With regard to the causative factor rainfall, two classes with 813 – 901mm and 901 - 994 mm have a higher C and FR values than the other classes and are the most susceptible classes (Table 1 and 2 ). Generally, slope classes > 20 0 , land use classes of grazing land, sparse forest, river and bush; lithology of colluvial deposit, weathered basalt, alluvial deposit and rhyolite and distance to stream classes of < 150 m buffers are the most contributing factor classes among the seven landslide factor classes.

Landslide susceptibility mapping using FR and WoE models

Frequency ratio model.

Map of each causative factor is prepared with the help of ArcGIS and then the frequency ratio values were calculated. The calculated FR values for each pixel in the LSI indicate the relative susceptibility to landslide occurrence. The higher pixel values of LSI have the higher landslide susceptibility while the lower pixel values will have lower susceptibility (Akgun et al. 2007 ). The landslide susceptibility index was calculated based on the frequency ratio values that have been determined in the training process that can be added in a raster calculator of ArcGIS as follows (Equation 8 ).

Where FR sl = frequency ratio value of slope, Fr as = frequency ratio value of aspect, = FR cu = frequency ratio value of curvature, FR li = frequency ratio value of lithology, FRlu = frequency ratio value of land use, FR rf = frequency ratio value of rainfall, FR ds = frequency ratio value of distance to stream.

The LSI values for the frequency ratio model in the study area range from 2.89 to 15.09. The LSI map is reclassified to prepare the landslide susceptibility map of the study area (Fig. 8 a). There are different types of classification methods such as natural break, equal interval, manual, standard deviation and quantile. In the current study, reliable results were obtained from natural breaks method. The result of other classification methods revealed the susceptibility classes with a high degree of exaggeration where large part of the study area fall into the high susceptibility class.

Therefore, the LSI values were classified into five susceptibility classes of very low (2.89 - 5.31), low (5.31 - 6.24), moderate (6.24 - 7.23), high (7.23 - 8.39) and very high (8.39 - 15.09) using the natural breaks method of classification. The result from Table 3 showed that 8.616% (16 km 2 ), 20.474%(38km 2 ), 29.537%(54.9km 2 ), 27.898% (51.8 km 2 ) and 13.474% (25 km 2 ) areas fall into the very low, low, moderate, high and very high susceptibility classes respectively. As Fig. 8 a clearly shows, the very low and low susceptibility classes are dominantly concentrated in the northwestern and southwestern plateau part of the study area including Welela Bahir, Shomeda, Agona and Jinjero Gedel localities. Similarly, the very high and high susceptibility classes are concentrated in the south central, southeastern and eastern part of the study area particularly in Asfa Meda (Majeta), Dubdubiya (Arata Gebriel) and Ditorka-Megersum respectively and scarcely distributed in the northern part of the study area at Guna-Gedeba Village and in the western part. Moderate susceptibility classes are mostly distributed throughout the study area. The high concentrations of landslides in those high and very high susceptibility classes of the aforementioned areas were due to the presence of colluvial and alluvial deposits, stream undercutting, scattered vegetation cover, man-made activities like intensive farming, deforestation and cultivation.

Weights of evidence model

The landslide susceptibility map of the study area by WoE model was produced based on the weighted values from the seven causative factors and the training landslide (Table 2 ). The difference between W + and W - is known as the weight of contrast which is designated by C = W + - W - . This reflects the overall spatial association between the causative factors and landslides. LSI map of the study area was prepared by summing the weight of contrast values (C) of all the seven causative factors using a raster calculator in ArcGIS as follows:

Where LSI = landslide susceptibility index; C sl = weight contrast value of slope, C as = weight contrast value of aspect, C cu = weight contrast value of curvature, C li = weight contrast value of lithology, C lu = weight contrast value of land use, C rf = weight contrast value of rainfall, C ds = weight contrast value of distance to stream.

The LSI values for the WoE model in the study area range from -7.84 to 4.52. The LSI map is reclassified by the natural breaks method of classification technique in order to prepare the landslide susceptibility map of the study area (Fig. 8 a). Then, the LSI values were classified in to five susceptibility zones of very low (-7.84 - -3.72), low (-3.72 - -1.83), moderate (-1.83 - -0.28), high (-0.28 – 1.17) and very high (1.17 – 4.52). The result from Table 4 showed that 8.448%(15.7 km 2 ), 21.408%(39.8km 2 ), 33.140%(61.5km 2 ), 23.787% (44.2 km 2 ) and 13.216% (24.5 km 2 ) area fall in the very low, low, moderate, high and very high susceptibility classes respectively.

Validation of the model

Without model validation, landslide susceptibility maps will not be meaningful. As a result, validation of the predictive model is an important step for landslide susceptibility mapping (Bui et al. 2012 ). A predictive model map was constructed by overlying 80% of the landslides (training) over the causative factors. This model was validated using validation landslides (20%) that were not used for building the model. There are various types of validation techniques for landslide susceptibility maps. In the current study, the performance of the LSMs produced by FR and WoE models were evaluated using Area Under the Curve (AUC) and Landslide Density Index (LDI).

Area under the curve (AUC)

The area-under-curve (AUC) method works by creating success rate and prediction rate curves (Lee 2005 ). Landslide susceptibility maps can be validated by comparing the susceptibility maps with both the training landslide (80%) and validation landslide (20%). The success and predictive rate curves can be created for both FR and WoE models. The success rate curve is based on the comparison between the predictive model and the training landslide. The predictive rate curve is based on the comparison between the predicted map and the validation landslide. The Area Under the Curve (AUC) of the success rate represents the quality of the model to reliably classify the occurrence of existing landslides whereas the AUC of the predictive rate explains the capacity of the proposed landslide model for predicting landslide susceptibility (Pamela et al. 2018 ). AUC was calculated by reclassifying LSI into 50 classes with descending order of the values of pixels in the study area and combined with a landslide inventory. Then the rate curves were drawn through the cumulative percentage of both the training and validation landslide (y-axis) and cumulative area percentage (x-axis). The result showed that both models exhibited very good performances. However, the FR model is better with a success rate of 89.8% and a predictive rate of 88.2% than the WoE model with a success rate of 86.5% and a predictive rate of 84.8% (Fig.  9 ).

Success and Predictive rate Curves of FR and WoE models

Landslide density index (LDI)

For validation of the model, landslide pixels which have not been used for constructing the models are generally considered as the future landslide area. In this work to check the validation of the landslide susceptibility model, the testing samples that consist of 20% of the landslide pixels were overlaid over the landslide susceptibility map. The landslide density index, which is the ratio between the percentage of landslide pixels and the percentage of class pixels in each class on landslide susceptibility map, was used to validate the model (Pham et al. 2015 ). If the value of the landslide density index is increased from low to a very high susceptibility classes, then the landslide susceptibility map is considered to be valid. LDI can be calculated using the formulae in eq. 10 below and its output was presented in Table  5 . The suitability of any susceptibility map can be validated if more percentages of landslides occur in the high and very high susceptibility zones as compared to other zones (Fayez et al. 2018 ).

From Table 5 , it can be observed that the landslide density values for very high susceptibility classes are 2.743 and 2.993 with respect to WoE and FR models which are remarkably higher than the other classes. In addition to this, there is a gradual decrement in landslide density values from very high to very low susceptibility classes (Fig.  10 ). This indicates the validity of the landslide susceptibility map. Can et al. ( 2005 ) and Bai et al. ( 2010 ) stated that the landslide data should lie in either the high or very high susceptibility classes for successful validation of a LSM. Table 5 , Figs.  10 and 11 illustrate the characteristics of susceptibility classes for validation of both FR and WoE models. In general, the presence of the highest landslide percentage and density in the very high landslide susceptibility class indicates the reliability of the landslide susceptibility maps produced from FR and WoE models.

Landslide density of FR and WoE models for both training and validation landslide

Bar diagram showing the landslide percentages in different landslide susceptibility classes

Landslide posed a significant impact at Simada District of South Gondar Zone in northwestern Ethiopia on human and animal lives, agricultural lands, settlements, infrastructures and also affected the social and economic aspects of the rural community. To investigate this problem, landslide susceptibility mapping has been carried out using FR and WoE models for proper land use planning, development and management of landslide prone areas. For this, a landslide inventory map of the study area with a total of 576 landslides was divided into training and validation landslides with 80 % and 20% respectively. Seven landslide causative factors including slope, aspect, curvature, lithology, land use, rainfall and distance to stream were considered to analyze, evaluate and establish the spatial relation of these factors with landslides. From FR values and WoE contrast values, it was possible to identify which factor classes are playing a significant role for the occurrence of landslides in the study area. The FR values that are greater than 1 and the WoE contrast (C) values that are greater than 0 were found in the factor classes of slope greater than 12°; curvature classes (-3.60) - (-0.001); aspect classes facing towards N (0 – 22.5), NE (22.5 – 67.5) and E (67.5 – 112.5); distance to stream classes (< 150m); land use classes (grazing land, river, sparse forest and bushes); lithology classes (colluvial deposit, alluvial deposit, weathered basalt and rhyolite), rainfall classes (813 – 901mm and 901 – 994 mm). The LSI map of the study area was prepared based on FR values and WoE contrast values in ArcGIS 10.4 using the spatial analyst tools of raster calculator for both FR and WoE models. The LSI map in each model was reclassified into five landslide susceptibility classes of low, low, moderate, high and very high based on the natural breaks method of classification to produce the final landslide susceptibility maps. The performance of the final landslide susceptibility maps produced by FR and WoE models were validated using Landslide Density Index (LDI) and Area Under the Curve (AUC) values. The result revealed that the very low, low, moderate, high and very high values of the landslide susceptibility map are comparable with Landslide Density Index. In case of AUC, the rate curves were drawn using the cumulative percentage of the landslide in the Y-axis and cumulative percentage of map area in the X-axis. The results showed that both models exhibited very good performance. However, the FR model, which showed a success rate of 89.8% and a prediction rate of 88.2%, is better than the WoE model with a success rate of 86.5% and a prediction rate of 84.8%. This study confirmed that the bivariate statistical methods of FR and WoE models were found to be simple and effective models for landslide susceptibility mapping in the Guna mountainous chain of Simada area. The landslide susceptibility maps of the study area were prepared with a scale of 1:50,000 which can be used by civil engineers, geologists, designers and decision-makers for regional land use planning, site selection and landslide prevention and mitigation purposes.

Recommendation

The present study showed the importance of integrating various factors that are responsible for landslide occurrence in the study area. However, the quality of landslide inventory and the causative factor maps should be improved with good quality in time and space. Landslide in the study area has affected the local people who are living near to mountainous area, valleys and gorges. Their animals were died, houses and agricultural lands were destroyed and both social and economic activities were affected. Hence, besides preparing the landslide susceptibility maps of the area, suggesting the necessary preventive measures in the high and very high susceptibility classes is very essential in order to reduce the impact of future landslide hazards in the area. Hence, this study recommends planting trees & vegetation, providing proper drainage, applying gabion and check dam, relocating people and creating public awareness. In order to implement these remedial measures, further study on the geotechnical properties of soils and rocks should be conducted in this area.

Availability of data and materials

Rainfall data was collected from National Metrology Agency of Ethiopia. Topographic Map was purchased from Ethiopian Geospatial Information Agency. DEM data was freely available from http://gdex.cr.usgs.gov/gdex/ website.

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Acknowledgments

The first author would like to thank Dr. Kifle Woldearegay, Mr. Azmeraw Wubalem and Mr. Leulalem Shano for thier useful Comments and suggestions to improve this research work. He would also like to thank Mizan Tepi University for providing the scholarship opportunity to pursue his MSc study at Addis Ababa Science and Technology University.

Mizan Tepi University partially funded the first author’s MSc thesis research work.

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Mersha, T., Meten, M. GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7 , 20 (2020). https://doi.org/10.1186/s40677-020-00155-x

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Journal of Civil Engineering Research

p-ISSN: 2163-2316    e-ISSN: 2163-2340

2019;  9(2): 51-57

doi:10.5923/j.jce.20190902.02

Landslide Causes and Corrective Measures – Case Study of the Sarajevo Canton

Amra Serdarevic 1 , Fuad Babic 2

1 Faculty of Civil Engineering, University of Sarajevo, Sarajevo, BiH

2 Development Planning Institute of the Sarajevo Canton, Sarajevo, BiH

Copyright © 2019 The Author(s). Published by Scientific & Academic Publishing.

The term landslide is well known to the general public and it is associated mainly with negative news of material and human casualties. This is why they are often mentioned in the media when they are already happened. Landslides are defined as the movement of soil or rock masses down the slope. It includes all forms of movement from landsliding, rolling of stones, falling rocks, rotational and translational sliding to the flow of different materials. Movements take place mainly on curved sliding surfaces. Moving of the mass on or along the slope (sliding) can be slow and barely noticeable in time, but it can also be very fast and destructive. The causes of landslides occurrence may be different, but in general, a landslide occurs when the active forces (mainly gravity) exceed the strength of soil materials and rocks that form the slope. Causes include factors that increase the effects of traction forces and factors that contribute to the weak or reduced strength of the slope material. Recently, mostly caused by extreme precipitation in a relatively short time, the numerous landslides in BiH have been activated. This paper presents the status and realization of landslide rehabilitation projects in Sarajevo Canton (SC).

Keywords: Landslides, Corrective measures, Mitigation, Monitoring

Cite this paper: Amra Serdarevic, Fuad Babic, Landslide Causes and Corrective Measures – Case Study of the Sarajevo Canton, Journal of Civil Engineering Research , Vol. 9 No. 2, 2019, pp. 51-57. doi: 10.5923/j.jce.20190902.02.

Article Outline

1. introduction, 2. overview of landslide characteristics, 3. landslides in the sarajevo canton, 3.1. reasons for landslides in the sarajevo canton, 3.2. land register – database, 3.3. landslides survey, 3.4. monitoring system, 4. conclusions.

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What causes landslides? Can we predict them to save lives?

by Pierre Rognon, The Conversation

A devastating landslide struck several remote villages in the mountainous Enga province in Papua New Guinea late last week.

While it is too early for official confirmation, estimates place the death toll between 690 and 2,000 people , with thousands more missing . That only a few bodies have been recovered serves as a tragic reminder of the destructive power of these events.

The ongoing search and rescue operations have proven challenging. As often with landslides, secondary slides and rock falls are hampering efforts in the search zone. There's also a lack of access to heavy digging machinery, and roads need to be cleared or repaired for assistance and equipment to arrive.

Even more critically, it is difficult to locate potential survivors, as landslides carry away buildings and their occupants in an unpredictable manner. What causes these devastating events and why are they so sudden and unpredictable?

What causes landslides?

Landslides happen when the pull from gravity exceeds the strength of the geomaterial forming the slope of a hill or mountain. Geomaterials can be as varied as rocks, sand, silt and clays.

Then, part of this slope starts sliding downhill. Depending on where the slope fails, the material sliding down can be just a few cubic meters or a few million cubic meters in volume.

Why do slopes fail? Most natural landslides are triggered by earthquakes or rainfall, or a combination of both.

Earthquakes shake the ground, stress it and weaken it over time. Rainwater can seep through the ground and soak it—the ground is often porous like a sponge—and add weight to the slope. This is why PNG is so prone to landslides, as it sits on an active fault and is subjected to heavy rainfalls.

Another adverse effect of water is erosion: the constant action of waves undercuts coastal slopes, causing them to fail. Groundwater can also dissolve rocks within slopes.

Humans can (and do) cause landslides in several ways, too. For example, deforestation has a negative impact on slope stability, as tree roots naturally reinforce the ground and drain water out. Also, mine blasts produce small earthquake-like ground vibrations that shake slopes nearby.

Why can't we predict landslides?

It's very difficult to predict and mitigate landslide risk effectively. The Enga landslide and the thousands of deadly and costly landslides occurring every year worldwide suggest so. Even in Australia—the flattest continent in the world —home insurance policies don't tend to cover landslide risk for a simple reason: this risk is difficult to estimate.

So what would it take to warn people of a coming landslide? You would need a prediction for earthquakes and rainfall, in addition to a perfect knowledge of the slope-forming geomaterial.

Under our feet, geomaterials may include multiple, entangled layers of various kinds of rocks and particulate materials, such as sand, silt and clays. Their strength varies from a factor of one to 1,000, and their spatial distribution dictates where the slope is likely to fail.

To accurately assess the stability of the slope, a three-dimensional mapping of these materials and their strengths is needed. No sensor can provide this information, so geologists and geotechnical engineers must deal with partial information obtained at a few selected locations and extrapolate this data to the rest of the slope.

The weakest link of the chain—such as an existing fracture in a rock mass—is easily missed. This is an inevitable source of uncertainty when trying to predict how much material might slip.

We do know that the larger the volume of a landslide, the farther its runout distance. But it's hard to gauge the exact size of a landslide, making predictions of runout distances and safe zones uncertain.

The question of "when will a landslide will occur" is also uncertain. Mechanical analysis enables us to estimate the vulnerability of a slope in a particular scenario, including earthquake magnitude and distribution of groundwater. But predicting if and when these triggers will happen is as "easy" as predicting the weather and seismic activity —a difficult task.

Unfortunately, all the money in the world can't buy accurate landslide predictions—especially in remote parts of the world.

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Authorities in Papua New Guinea search for safer ground for thousands of landslide survivors

The United Nations estimated 670 villagers died in the disaster that immediately displaced 1,650 survivors.

Authorities in Papua New Guinea were searching on Wednesday for safer ground to relocate thousands of survivors at risk from a potential second landslide in the country's highlands, while the arrival of heavy earth-moving equipment at the disaster site where hundreds are buried has been delayed, officials said.

Emergency responders say that up to 8,000 people might need to be evacuated as the mass of boulders, earth and splintered trees that crushed the village of Yambali in the South Pacific island nation’s mountainous interior on Friday becomes increasingly unstable.

But an evacuation center near Yambali in Enga province only had room for about 50 families, said Justine McMahon, country director for the humanitarian agency CARE International.

“For the number of people that they anticipate having to help, they actually need more land and I understand the authorities are trying to identify places now,” McMahon said.

Enga provincial disaster committee chairperson and provincial administrator Sandis Tsaka told The Associated Press he would not know how many villagers had been evacuated until late Wednesday.

Also Read | India to send $1 million aid to landslide-hit Papua New Guinea

The unstable ground was also impacting the humanitarian response, said Kate Forbes, president of the International Federation of Red Cross and Red Crescent Societies.

“Right now, the issue is, I understand, ... safety and access,” Forbes told reporters in Manila in the Philippines.“We have to be sure that the land is somewhat stabilized before we can send our workers in to a great deal of extent,” she added.

Papua New Guinea’s government has told the United Nations it thinks more than 2,000 people were buried . Six bodies had been retrieved from the rubble by Tuesday. Papua New Guinea’s military earth-moving equipment had been expected to arrive at the scene on Tuesday after traveling from the city of Lae, 400 kilometers (250 miles) to the east. But that plan changed when a bridge between the Enga provincial capital Wabag and the nearest airstrip at Mount Hagen collapsed late Monday for reasons that have yet to be explained.

A detour adds two or three hours to the journey for aid convoys taking supplies to Mount Hagan to the devastated village. It also has prevented the heavy equipment being trucked from Lae.

Five to 10 heavy earth-moving machines were now expected to be on the scene by Thursday, the Papua New Guinea Defense Force said.

Also Read | Everything you need to know about the landslide in Papua New Guinea

A team of 40 military engineers and medical staff reached Wabag on Tuesday night and were making the two-hour drive to Yambil on Wednesday. The province’s main highway remains blocked by the landslide beyond Yambali.

The team has begun negotiating with the villagers for permission to start digging.

Traumatized villagers are divided over whether heavy machinery should be allowed to dig up and potentially further damage the bodies of their buried relatives.

An excavator donated by a local builder Sunday became the first piece of heavy earth-moving machinery brought in to help villagers who have been digging with shovels and farming tools to find bodies.

Papua New Guinea is a diverse, developing nation with 800 languages and 10 million people who are mostly subsistence farmers.

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Papua New Guinea says Friday’s landslide buried more than 2,000 people and formally asks for help

A Papua New Guinea government official told the United Nations over 2,000 people were believed to have been buried alive by Friday’s landslide and has asked for international help.

This May 27, 2024, satellite image provided by Maxar Technologies shows the recent landslide in the Enga region of northern Papua New Guinea that killed hundreds of people and buried part of the Yambali village. (Maxar Technologies via AP)

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In this image supplied by the International Organization for Migration, villagers search amongst the debris from a landslide in the village of Yambali in the Highlands of Papua New Guinea, Monday, May 27, 2024. (Mohamud Omer/International Organization for Migration via AP)

This May 27, 2024, satellite image provided by Maxar Technologies shows the recent landslide in the Enga region of northern Papua New Guinea that killed and wounded hundreds of people and buried part of the Yambali village. (Maxar Technologies via AP)

In this image supplied by the International Organization for Migration, villagers react after a body was discovered amongst the debris form a landslide in the village of Yambali in the Highlands of Papua New Guinea, Monday, May 27, 2024. (Mohamud Omer/International Organization for Migration via AP)

MELBOURNE, Australia (AP) — A Papua New Guinea government official has told the United Nations that more than 2,000 people are believed to have been buried alive by last Friday’s landslide and has formally asked for international help.

The government figure is roughly triple the U.N. estimate of 670 killed by the landslide in the South Pacific island nation’s mountainous interior. The remains of only five people had been recovered by Monday, local authorities reported. It was not immediately clear why the tally of six reported on Sunday had been revised down.

In a letter to the United Nations resident coordinator dated Sunday and seen by The Associated Press, the acting director of the country’s National Disaster Center, Luseta Laso Mana, said the landslide “buried more than 2,000 people alive” and caused “major destruction” in Yambali village in Enga province.

Estimates of the casualties have varied widely since the disaster occurred, and it was not immediately clear how officials arrived at the number of people affected.

The International Organization for Migration, which is working closely with the government and taking a leading role in the international response, has not changed its estimated death toll of 670 released on Sunday, pending new evidence.

“We are not able to dispute what the government suggests but we are not able to comment on it,” said Serhan Aktoprak, chief of the U.N. migrant agency’s mission in Papua New Guinea.

“As time goes in such a massive undertaking, the number will remain fluid,” Aktoprak added.

U.N. Secretary-General António Guterres sent “heartfelt condolences” to the families of the victims and the people and government of Papua New Guinea and said the U.N. and its partners are supporting the government’s response efforts, and “the United Nations stands ready to offer additional assistance at this challenging time,” U.N. spokesman Stephane Dujarric said Monday.

The death toll of 670 was based on calculations by Yambali village and Enga provincial officials that more than 150 homes had been buried by the landslide. The previous estimate had been 60 homes.

The office of Papua New Guinea Prime Minister James Marape did not respond Monday to a request for an explanation of what the government estimate of 2,000 was based on. Marape has promised to release information about the scale of the destruction and loss of life when it becomes available.

Determining the scale of the disaster is difficult because of challenging conditions on the ground, including the village’s remote location, a lack of telecommunications and tribal warfare throughout the province which means international relief workers and aid convoys require military escorts.

At least 26 tribal warriors and mercenaries were killed in a battle between two warring tribes in Enga in February, as well as an unconfirmed number of bystanders.

The national government’s lack of reliable census data also adds to the challenges of determining how many are potentially dead.

The government estimates Papua New Guinea’s population at around 10 million people, although a U.N. study, based on data including satellite photographs of roof tops, estimated in 2022 it could be as high as 17 million. An accurate census has not been held in the nation in decades.

The landslide also buried a 200-meter (650-foot) stretch of the province’s main highway under debris 6 to 8 meters (20 to 26 feet) deep, creating a major obstacle for relief workers.

Mana said the landslide would have a major economic impact on the entire country.

An excavator donated by a local builder Sunday became the first piece of heavy earth-moving machinery brought in to help villagers who have been digging with shovels and farming tools to find bodies. Working around the still-shifting debris is treacherous.

“The situation remains unstable” due to the shifting ground, “posing ongoing danger to both the rescue teams and survivors alike,” Mana wrote to the United Nations.

Mana and Papua New Guinea’s defense minister, Billy Joseph, flew on Sunday in an Australian military helicopter from the capital of Port Moresby to Yambali, 600 kilometers (370 miles) to the northwest, to gain a firsthand perspective of what is needed.

Mana’s office posted a photo of him at Yambali handing a local official a check for 500,000 kina ($130,000) to buy emergency supplies for 4,000 displaced survivors.

The purpose of the visit was to decide whether Papua New Guinea’s government needed to officially request more international support.

Earth-moving equipment used by Papua New Guinea’s military was being transported to the disaster scene, 400 kilometers (250 miles) from the east coast city of Lae.

Traumatized villagers are divided over whether heavy machinery should be allowed to dig up and potentially further damage the bodies of their buried relatives, officials said.

Associated Press journalist Adam Schreck in Bangkok contributed to this report.

New research shows soil microorganisms could produce additional greenhouse gas emissions from thawing permafrost

As the planet has warmed, scientists have long been concerned about the potential for harmful greenhouse gasses to seep out of thawing Arctic permafrost. Recent estimates suggest that by 2100 the amount of carbon dioxide and methane released from these perpetually frozen lands could be on par with emissions from large industrial countries. However, new research led by a team of Colorado State University microbiome scientists suggests those estimates might be too low.

Microorganisms are responsible for the process that will generate greenhouse gasses from thawing northern peatlands, which contain about 50% of the world's soil carbon. For now, many of the microbes in this environment are frozen and inactive. But as the land thaws the microbes will "wake up" and begin to churn through carbon in the ground. This natural process, known as microbial respiration, is what produces the carbon dioxide and methane emissions forecasted by climate modelers.

Currently, these models assume that this community of microorganisms -- known as a microbiome -- will break down some types of carbon but not others. But the CSU-led work published this week in the journal Nature Microbiology provides new insight into how these microbes will behave once activated. The research demonstrates that the soil microbes embedded in the permafrost will go after a class of compounds previously thought to be untouchable under certain conditions: polyphenols.

"There were these pools of carbon -- say, donuts, pizza and chips -- and we were comfortable with the idea that microbes were going to use this stuff," said Bridget McGivern, a CSU postdoctoral researcher and the paper's first author. "But then there was this other stuff, spicy food; we didn't think the organisms liked spicy food. But what our work is showing is that actually there are organisms that are eating it, and so it's not going to just stay as carbon, it's going to be broken down."

More carbon being broken down by microbial respiration will produce additional greenhouse gas emissions. But this new finding has other implications, too. Some scientists had previously theorized that adding polyphenols to the thawing Arctic permafrost could potentially "turn off" these microorganisms altogether, effectively trapping a massive cache of potentially problematic carbon in the ground. The concept is known as the enzyme latch theory.

That no longer appears to be a viable option, said Kelly Wrighton, associate professor in the College of Agricultural Sciences' Department of Soil and Crop Sciences, whose lab led the work. "Not only did we think these microbes didn't eat polyphenols," Wrighton said, "we thought that if the polyphenols were there it was like they were toxic and would lock the microbes into inactivity."

The soil microbiome has often been considered something of a black box due of its complexity. Wrighton hopes this new information about the role of polyphenols in permafrost helps shift that perception. "I'd like to move past these black box assumptions," she said. "We can't engineer solutions if we don't understand the underlying wiring and plumbing of a system."

Probing the permafrost in Sweden

Unlocking the relationship between soil microbes and polyphenols has been years in the making for McGivern, who began examining this topic while working on her doctoral degree in Wrighton's lab in 2017.

McGivern started with a simple question. Scientists presumed that without oxygen, soil microbes could not break down polyphenols. Gut microbes, however, don't need oxygen to churn up the compound -- that's how humans extract healthy antioxidant benefits from polyphenol-rich substances such as chocolate and red wine. McGivern wondered why the process would be different in soils, a question that is particularly relevant to permafrost or waterlogged lands that contain little or no oxygen.

"The motivation for a lot of my Ph.D. was how could these two things exist?" McGivern said. "Organisms in our gut can breakdown polyphenols but organisms in the soil can't? The reality was that nobody in soils had really ever looked at it."

McGivern and Wrighton successfully tested the theory in a lab experiment and published a proof of concept study in 2021. The next step was testing it in the field. The team gained access to core samples from a research site in northern Sweden, a place that scientists have used for years to examine questions related to permafrost and the soil microbiome.

But before McGivern could look for evidence of polyphenol degradation in the core samples, she first had to create a database of gene sequences that corresponded to polyphenol metabolism. McGivern mined thousands of pages of existing scientific literature, cataloging the enzymes in cattle, the human gut, and some soils that were known to be responsible for the process. Once she built the database, McGivern compared the results to the gene sequences expressed by the microbes in the core samples. Sure enough, she said, polyphenol metabolism was happening.

"What we found was that genes across 58 different polyphenol pathways were expressed," McGivern said. "So, we're saying not only can the microorganisms potentially do it, but they actually are, in the field, expressing the genes for this metabolism."

Still, more work is needed, McGivern said. They don't know what might constrain the process or the rates at which the metabolism is happening -- both important factors for eventually quantifying the amount of additional greenhouse gas emissions that could be released from permafrost.

"The whole point of this is to build a better predictive understanding so that we have a framework we can actually manipulate," Wrighton said. "The climate crisis we're facing is so fast. But can we model it? Can we predict it? The only way we're going to get there is to actually understand how something works."

• Global Warming
• Air Quality
• Environmental Issues
• Geochemistry
• Climate change mitigation
• Greenhouse gas
• Climate engineering
• Fossil fuel
• Sulfur hexafluoride
• Carbon dioxide

Story Source:

Materials provided by Colorado State University . Original written by Christopher Outcalt. Note: Content may be edited for style and length.

Journal Reference :

• Bridget B. McGivern, Dylan R. Cronin, Jared B. Ellenbogen, Mikayla A. Borton, Eleanor L. Knutson, Viviana Freire-Zapata, John A. Bouranis, Lukas Bernhardt, Alma I. Hernandez, Rory M. Flynn, Reed Woyda, Alexandra B. Cory, Rachel M. Wilson, Jeffrey P. Chanton, Ben J. Woodcroft, Jessica G. Ernakovich, Malak M. Tfaily, Matthew B. Sullivan, Gene W. Tyson, Virginia I. Rich, Ann E. Hagerman, Kelly C. Wrighton. Microbial polyphenol metabolism is part of the thawing permafrost carbon cycle . Nature Microbiology , 2024; DOI: 10.1038/s41564-024-01691-0

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More Than 670 Feared Dead in Papua New Guinea Landslide, UN Agency Says

May 24, 2024. Emmanuel Eralia via REUTERS

By Samuel McKeith

SYDNEY (Reuters) -More than 670 people are assumed to have died in Papua New Guinea's massive landslide, the U.N. migration agency estimated on Sunday as rescue efforts continued.

Media in the South Pacific nation north of Australia had previously estimated Friday's landslide had buried more than 300 people. But more than 48 hours later the International Organization for Migration (IOM) said the death toll may be more than double that, as the full extent of the destruction is still unclear and continuing dangerous conditions on the ground are hampering aid and rescue efforts.

Only five bodies had been retrieved from the rubble so far.

The agency based its death toll estimates on information provided by officials at Yambali Village in the Enga province, who say more than 150 houses were buried in Friday's landslide, Serhan Aktoprak, the chief of the agency's mission in Papua New Guinea said in an email statement.

"Land is still sliding, rocks are falling, ground soil is cracking due to constant increased pressure and ground water is running thus the area is posing an extreme risk for everyone," Aktoprak said.

More than 250 houses nearby have been abandoned by the inhabitants, who had taken temporary shelter with their relatives and friends, and some 1,250 people have been displaced, the agency said.

"People are using digging sticks, spades, large agricultural forks to remove the bodies buried under the soil," Aktoprak said.

The IOM said an elementary school, small businesses and stalls, a guesthouse, and a petrol station were also buried.

The U.N.'s Papua New Guinea office said five bodies were retrieved from an area where 50 to 60 homes had been destroyed, and a number of injured reported, including at least 20 women and children.

IOM said the community in this village was relatively young and it's feared that the most fatalities would be children of 15 years or younger.

COMMUNITY GRIEVING

Social media footage posted by villagers and local media teams show people clambering over rocks, uprooted trees and mounds of dirt searching for survivors. Women could be heard weeping in the background.

The landslide hit a section of highway near the Porgera gold mine, operated by Barrick Gold through Barrick Niugini Ltd, its joint venture with China's Zijin Mining.

The Porgera Highway remains blocked, IOM said, and the only way to reach the Porgera Gold Mine and other localities cut off from the rest of Enga Province is via helicopter.

The geographic remoteness and the tough, hilly terrain is slowing rescue and aid efforts.

The government and the PNG Defence Force engineering team is on the ground now, but heavy equipment like excavators, required for the rescue, are yet to reach the village. IOM said the community may not allow use of excavators until they consider they had fulfilled their mourning and grieving obligations.

"People are coming to terms with the fact that the people under the debris are now all but lost," IOM said in an earlier status update by email.

The government plans to establish two care/evacuation centres, each on one side of the landslide affected area to host the displaced who may need shelter.

A humanitarian convoy has started distributing bottled water, food, clothing, hygiene kits, kitchen utensils, tarpaulins, as well as personal protective equipment.

Aid group CARE Australia said late on Saturday that nearly 4,000 people lived in the impact zone but the number affected was probably higher as the area is "a place of refuge for those displaced by conflicts" in nearby areas.

At least 26 men were killed in Enga Province in February in an ambush amid tribal violence that prompted Prime Minister James Marape to give arrest powers to the country's military.

The landslide left debris up to 8 metres (25 feet) deep across 200 square km (80 square miles), cutting off road access and making relief efforts difficult, CARE said.

Marape has said disaster officials, the Defence Force and the Department of Works and Highways were assisting with relief and recovery efforts.

(Reporting by Sam McKeith in Sydney; writing by Praveen Menon; Editing by Chris Reese, William Mallard and David Evans)

Copyright 2024 Thomson Reuters .

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A systematic review for assessing the impact of climate change on landslides: research gaps and directions for future research

• Published: 29 September 2023
• Volume 32 , pages 165–185, ( 2024 )

Cite this article

• Aastha Sharma   ORCID: orcid.org/0000-0001-5963-9106 1 ,
• Haroon Sajjad   ORCID: orcid.org/0000-0002-2007-1266 1 ,
• Roshani   ORCID: orcid.org/0000-0002-0330-4232 1 &
• Md Hibjur Rahaman   ORCID: orcid.org/0000-0002-8999-7497 1  

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The magnitude and intensity of landslides due to changing climate have created environmental and socio-economic implications for society. Through an in-depth analysis of the existing research on landslides in a changing climate from 1996 to 2021, this paper aims to carry out bibliometric and thematic analyses, identify the research gaps in the existing literature, and suggest a future framework for climate change-induced landslide risk assessment and mitigation. The data for review was collected from the Web of Science and Scopus platforms using a set of relevant keywords. After meeting the exclusion and inclusion criteria, 200 studies were finally selected to analyze the current state of research. The findings revealed that most of the reviewed studies focused on economic vulnerability to landslides, while social and ecological aspects of vulnerability at the micro-scale were scant in the past literature. Uncertainty in landslide-climate modeling, lack of advanced models for predicting landslide risk, and lack of early warning systems were identified as the major research gaps. A holistic methodological approach is proposed for assessing landslide risk and devising landslide mitigation strategies. The identified research gaps and the proposed framework may help in the future progression of climate change-induced landslide research in spatial information science.

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A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Impact of climate change on biodiversity loss: global evidence

An overview of land degradation, desertification and sustainable land management using GIS and remote sensing applications

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Aastha Sharma, Haroon Sajjad,  Roshani & Md Hibjur Rahaman

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Sharma, A., Sajjad, H., Roshani et al. A systematic review for assessing the impact of climate change on landslides: research gaps and directions for future research. Spat. Inf. Res. 32 , 165–185 (2024). https://doi.org/10.1007/s41324-023-00551-z

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Received : 11 July 2023

Revised : 18 September 2023

Accepted : 19 September 2023

Published : 29 September 2023

Issue Date : April 2024

DOI : https://doi.org/10.1007/s41324-023-00551-z

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