Landslide Susceptibility Classes Research Articles

Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China.

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.

Landslide Susceptibility Mapping using Geographic Information System (GIS) and Remote Sensing Data in North Luwu, South Sulawesi Province, Indonesia

Climate, topography, and rock conditions in Indonesia are relatively diverse, both physical and chemical, these conditions can cause adverse consequences such as floods, landslides, forest fires, and droughts. The Information from Volcanology and Geological Disaster Mitigation on Monday, July 13, 2020, flash floods and flow of debris due to landslides have occurred in Masamba and Baebunta, North Luwu, South Sulawesi Province. The purpose of this paper is to map the distribution of landslide susceptibility area using remote sensing and GIS data. The Method used to analyze the landslide susceptibility estimation model refers to the Indonesian Center for Agricultural Land Resources Research and Development (ICALRD). The parameters used in this model are rainfall, soil type, rock type, slope, land use, and land movement. The ICALRD Landslide Susceptibility Estimation Model in study area shows that there are three classes of landslide susceptibility, such as low, medium, and high susceptibilities. There are six subdistricts which are in medium to high landslide susceptibility, and the others are low to medium landslide susceptibility. The locations of landslides and flash floods that occurred in Masamba and Baebunta indicate areas with medium to high landslide susceptibility.

Susceptibility Assessment of Landslides in the Loess Plateau Based on Machine Learning Models: A Case Study of Xining City

Landslide susceptibility assessment can effectively predict the spatial distribution of potential landslides, which is of great significance in fields such as geological disaster prevention, urban planning, etc. Taking Xining City as an example, based on GF-2 remote sensing image data and combined with field survey data, this study delineated the spatial distribution range of developed landslides. Key factors controlling landslides were then extracted to establish a landslide susceptibility assessment index system. Based on this, the frequency ratio (FR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models were applied to spatially predict landslide susceptibility with slope units as the basis. The main results are as follows: (1) The overall spatial distribution of landslide susceptibility classes in Xining City is consistent, but the differences between different landslide susceptibility classes are significant. (2) The high-susceptibility area predicted by the FR-RF model is the largest, accounting for 15.48% of the total study area. The prediction results of the FR-ANN and FR-SVM models are more similar, with high-susceptibility areas accounting for 13.96% and 12.97%, respectively. (3) The accuracy verification results show that all three coupled models have good spatial prediction capabilities in the study area. The order of landslide susceptibility prediction capabilities from high to low is FR-RF model > FR-ANN model > FR-SVM model. This indicates that in the study area, the FR-RF model is more suitable for carrying out landslide susceptibility assessment.

Critical area identification and dynamic process simulation for landslide hazard chain formation in the upstream Jinsha River

The upper reaches of the Jinsha River, with their complex terrain and active tectonic movements, are vulnerable to landslide-induced hazard chain events, which endanger the safety of residents and infrastructure construction. Based on the analysis of the development background of the hazard chain in the upstream area of the Jinsha River, five factors, including the lithology, distance to faults, distance to rivers, peak ground acceleration, and slope degree, were selected to identify the critical landslide-prone areas. Principal component and grey correlation analyses were then conducted to determine the contributions of these different factors. Based on ArcGIS, the study zone was categorized into five classes of landslide susceptibility: very high, high, moderate, low, and very low. The identification of the critical target areas for landslide hazard chain formation showed satisfactory accuracy. The very high- and high-susceptibility areas are concentrated along the Jinsha River. The dynamic process of a typical landslide in a very high-susceptibility area was numerically simulated using OpenLISEM. The high-precision Baige landslide data of the study area were used to calibrate the practicality of the input mass parameters, including cohesion, internal friction angle, D50, and D90. The movement and accumulation processes of a typical landslide were then numerically simulated with the verified data. The entire landslide accumulation covers an area of 0.45 km2, with a length of 1,600 m and a width of 270 m. Thus, the OpenLISEM model, which combines mass, topography, and landcover parameters, is feasible for the numerical simulation of landslide dynamic processes. The prediction of the dynamic processes and accumulation morphology of landslides can provide a reference for the formation processes and mechanisms of the landslide-induced hazard chain in the upper Jinsha River.

ANALISIS TINGKAT KERAWANAN DAN RESILIENSI MASYARAKAT TERHADAP LONGSORLAHAN DI KECAMATAN JATIYOSO KABUPATEN KARANGANYAR TAHUN 2020

This study aims to (1) the level of vulnerability to landslides in Jatiyoso District, Karanganyar Regency in 2020, (2) the level of community resilience to landslides in Jatiyoso District, Karanganyar Regency in 2020, and (3) disaster mitigation directions carried out on landslide vulnerability in Jatiyoso District, Karanganyar Regency. The data analysis technique used in this study was descriptive qualitative. The results of this study are (1) The level of landslide susceptibility in the Jatiyoso District into three classes of landslide susceptibility: low, medium, and high. (2) There are two levels of community resilience to landslides, namely, a moderate level of resilience (55.56% from Jatiyoso District) and high resilience level (44.44% from Jatiyoso District). (3) The direction of disaster mitigation consists of zone II (moderate vulnerability), the need for restrictions on settlements, evaluation of the suitability of agricultural land, and the need for evaluation paths to refugee places, and zone III (high vulnerability) there needs to be a prohibition on development and this area should be specially used for conservation.

Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam

Ban Khoang is a mountainous commune in Sa Pa district located in the central part of Lao Cai province, Vietnam. Landslides occur frequently in this area and seriously affect the local living conditions. To help the local authority in developing a landslide disaster action plan, the statistical index method for landslide susceptibility mapping is applied. As the result, the landslide susceptibility zonation (LSZ) map was created. The LSZ map indicates that areas of low, moderate, high and very high landslide susceptibility zones are, respectively, 20.3 km2, 12.4 km2, 15.4 km2, and 5.2 km2; most of the observed landslide areas that are well predicted belong to high or very high landslide susceptibility classes. In detail, 80% observed landslide areas and 78.57% number of observed landslides were well predicted, and the area (AUC) under the receiver operating characteristic (ROC) curve obtained 80.3%. Hence, the high and very high landslide susceptibility classes in the LSZ map can be considered highly believable, and the LSZ map will be reliable to use in the practice.

Susceptibility Mapping of Typical Geological Hazards in Helong City Affected by Volcanic Activity of Changbai Mountain, Northeastern China

The purpose of this paper was to produce the geological hazard-susceptibility map for the Changbai Mountain area affected by volcanic activity. First, 159 landslides and 72 debris flows were mapped in the Helong city are based on the geological disaster investigation and regionalization (1:50,000) project of Helong City. Then, twelve landslide conditioning factors and eleven debris flow conditioning factors were selected as the modeling variables. Among them, the transcendental probability of Changbai Mountain volcanic earthquake greater than VI degrees was used to indicate the relationship between the geological hazard-susceptibility and Changbai Mountain volcanic earthquake occurrence. Furthermore, two machine learning models (SVM and ANN) were introduced to geological hazard-susceptibility modeling. Receiver operating characteristic curve, statistical analysis method, and five-fold cross-validation were used to compare the two models. Based on the modeling results, the SVM model is the better model for both the landslide and debris flow susceptibility mapping. The results show that the areas with low, moderate, high, and very high landslide susceptibility are 31.58%, 33.15%, 17.07%, and 18.19%, respectively; and the areas with low, moderate, high, and very high debris flow susceptibility are 25.63%, 38.19%, 23.47%, and 12.71%, respectively. The high and very high landslide and debris flow susceptibility classes make up 85.54% and 80.55% of the known landslides and debris flow, respectively. Moreover, the very high and high landslide and debris flow susceptibility are mainly distributed in the lower elevation area, and mainly distributed around the cities and towns in Helong City. Consequently, this paper will be a useful guide for the deployment of disaster prevention and mitigation in Helong city, and can also provide some reference for evaluation of landslide susceptibility in other volcanically active areas.

Assessment of Submarine Landslide Susceptibility in the Sea Area of Zhoushan

The research on the submarine landslide susceptibility has important guiding significance for the site selection and construction of marine engineering. The Zhoushan Archipelago New Area is dominated by the marine industry, and the marine engineering under its jurisdiction is densely covered. This research selected the surrounding sea area of Zhoushan island as the study area to evaluate submarine landslide susceptibility. The factors such as seismic peak acceleration, proximity to fault zone, average wave height, slope and sediment types were selected to construct the evaluation index system, all of which were considered to be influential in the occurrence of landslides. Meanwhile, the AHP method was used to calculate the weight of each index factor. In combination with geographical information system, the study area has been classified into five classes of relative landslide susceptibility, namely, extremely low, low, medium, medium-high and high. The research result showed that: (1) The high susceptibility zone in the evaluation result was consistent with the position of historical landslide. (2) The five classes in the result of submarine landslide susceptibility assessment for study area accounted for 1.17%, 1.97%, 9.08%, 56.05% and 31.74% of the total area from high to low, and the area with susceptibility above middle grade mainly distributed in the underwater bank slope around the islands or the slopes position of the tidal current scours. (3) This research was a preliminary study on the susceptibility assessment of regional submarine landslides. It was limited by the availability of evaluation index data and submarine landslide data accumulation in the region. Besides that, the evaluation model was influenced by experience greatly, so the evaluation result had certain subjectivity.

Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models

Landslide susceptibility mapping is very important for landslide risk evaluation and land use planning. Toward this end, this paper presents a case study in Ningqiang County, Shanxi Province, China. Slope units were selected as the basic mapping units. A traditional statistical certainty factor model (CF), a machine learning support vector machine model (SVM) and random forest model (RF), along with a hybrid CF-SVM model and a CF-RF model were applied to analyze landslide susceptibility. Firstly, 10 landslide conditioning factors were selected, namely slope-angle, altitude, slope aspect, degree of relief, lithology, distance to rivers, distance to faults, distance to roads, average annual rainfall and normalized difference vegetation index. The 23,169 slope units were generated from a Digital Elevation Model and the corresponding 10 conditioning factor layers were produced from both geological and geographical data. Then, landslide susceptibility mapping was carried out using the five models, respectively. Next, the landslide density (LD), frequency ratio (FR), the area under the curve (AUC) and other indicators were used to validate the rationality, performance and accuracy of the models. The results showed that the susceptibility maps produced from the different models were all reasonable. In each map, the LD and FR were greatest in the zones classed as having very high landslide susceptibility, followed by the high, moderate, low and very low landslide susceptibility classes, respectively. From the comparison of the different maps and ROC curves, the RF model based on slope units was the most appropriate for landslide susceptibility mapping in the study area. It was also found that the combination of weaker learner model (CF model here) with a stronger learner model (SVM and RF model here) can impact the applicability of the stronger model.

MODELAGEM DA SUSCEPTIBILIDADE DE ESCORREGAMENTOS UTILIZANDO O MÉTODO DE PESOS DE EVIDÊNCIA EM UMA REGIÃO MONTANHOSA DE CLIMA TROPICAL

Escorregamentos são fenômenos naturais que ocorrem em regiões montanhosas e podem causar grandes catástrofes socioeconômicas. A identificação das áreas susceptíveis a escorregamentos é uma etapa essencial da gestão de riscos e auxilia a implementação de projetos que visam mitigar, ou mesmo evitar os danos causados por estes eventos. Este trabalho avalia a susceptibilidade a escorregamentos translacionais pelo método estatístico bivariado de Pesos de Evidência, assim como o controle individual de oito fatores predisponentes em duas bacias piloto no maciço da Tijuca, na cidade do Rio de Janeiro (RJ). Cinco modelos de susceptibilidade foram produzidos pela combinação dos fatores predisponentes e tiveram sua capacidade preditiva testada pelo cálculo da área abaixo da curva (AAC). A maior parte dos escorregamentos mapeados ocorreram nas cabeceiras de drenagem das bacias piloto, nas encostas com ângulo entre 34° e 51°, sustentadas principalmente por intrusões graníticas e coberta por floresta preservada. O mapa final possui cinco classes de susceptibilidade e demonstrou ser eficaz na previsão dos escorregamentos. O método é robusto e evita subjetividades, ao mesmo tempo que é bastante versátil. Apesar de pouco utilizado no Brasil, o método é apropriado ao contexto de complexa geodiversidade e carência de dados geotécnicos a nível regional e local.

Mapping landslide susceptibility at national scale by spatial multi-criteria evaluation

The representation of terrain propensity to generate landslides, meaning the mapping of landslide susceptibility, represents a first step in the assessment of the risk induced by these geomorphological hazards. Results of the numerous studies differ according to the available input data, the applied methodology, the level of detail or the scale of analysis. The main goal of the present study is to provide a new landslide susceptibility map for the national territory of Romania. The application of the European Landslide Susceptibility (ELSUS) mapping methodology, based on the analytical hierarchy process (AHP), using more accurate and higher spatial resolution input data, led to the achievement of a landslide susceptibility map within individual climate-physiographic zones across the country. The landslide susceptibility is computed by combining the information regarding landslide frequency with a Spatial Multi-Criteria Evaluation (SMCE) model based on three predictors (slope angle, lithology and land cover). The continuous landslide susceptibility model was validated by receiver operating characteristic (ROC) analysis, which indicated a better performance in the plateau, hilly and plain areas (AUC = 0.750) compared to the mountain area. Also, an independent sample validation was performed for testing the consistency of the landslide susceptibility classes. The classified susceptibility map shows patterns that are in agreement with terrain reality.

Mapping Landform and Landslide Susceptibility Using Remote Sensing, GIS and Field Observation in the Southern Cross Road, Malang Regency, East Java, Indonesia

There has been an increasing trend of land area being brought under human’s use over time. This situation has led the community to carry out land-use development activities in landslide hazard-prone areas. The use of land can have a positive impact by increasing economic conditions, but it can have negative impacts on the environment. Therefore, this study aimed to identify the landslide hazard, focusing on the development of a landform map to reduce the risk of landslide disaster in JLS, Malang Regency. The integration of remote sensing and geographic information systems, as well as field observation, were used to create a landform map and a landslide susceptibility map. Using the geomorphological approach as a basic concept in landform mapping, the morphology, morphogenesis, and morphoarrangement conditions were obtained from the remote sensing data, GIS, and field observation, while morphochronological information was obtained from a geological map. The landslide susceptibility map was prepared using 11 landslide conditioning factors by employing the index of entropy method. Thirty-nine landform units were successfully mapped into four landslide susceptibility classes. The results showed that the study area is dominated by a high level of landslide susceptibility with a majority of moderate to strongly eroded hill morphology. It also reaffirms that landform mapping is a reliable method by which to investigate landslide susceptibility in JLS, Malang Regency.

A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas

Landslide susceptibility zonation (LSZ) has generally been regarded as the appropriate stride to begin scientific studies in mountainous terrains to alleviate the socio-economic consequences of landslides. Application of machine learning (ML) with geographic information system (GIS) is a promising fusion of technologies, for spatial prediction of landslide susceptibility with high precision, and has been applied widely in the past. However, the literatures of ML and GIS-based LSZ gives a fuzzy conclusion upon the righteous choice of ML technique among many state-of-the-art techniques, and do not present a probe on the aptitude of ML models for township level LSZ attempts. This research investigates such concern with a case study to figure out a robust technique, which can be a benchmark approach in future case studies and various comparisons strives across the sundry genre of ML. For that, the present attempt has been anchored to four different supervised ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) of neural network (NN) genre, classical ML algorithm of support vector machine (SVM) and extreme learning adaptive neuro fuzzy inference system (ELANFIS) of neuro-fuzzy system genre. The Mussoorie Township, a famed hill station in the Indian State of Uttarakhand was chosen as the area for case study. A total of 13 landslide susceptibility maps (LSM) were produced. Spatial performance of these maps was compared and statistically validated with the help of landslide inventory of the study area. Amongst the LSMs, the LSM-ELANFIS-VII of ELANFIS model with 11 number of membership functions (MF) was found to be in better agreement with all the validation measures performed. In addition to the satisfactory performance on validation, the LSMs produced through ELANFIS display a unique trace of geomorphological features on it along with pragmatic scattering of landslide susceptibility classes - an omen that exhorts graduation of GIS-based LSZ to ensemble neuro-fuzzy ML models.

Probabilistic modeling of shallow landslide initiation using regional scale random fields

Regional mapping of landslide susceptibility aims to identify zones of potential instability across geological settings. Given their predictive capabilities, physically based, deterministic models are useful tools for landslide triggering studies at regional scale. However, they rely on detailed input parameters that are rarely available for large areas. To address these limitations, this work proposes a computational framework to incorporate the spatial uncertainty of input data into physically based, landslide hazard zonation models through the use of regional scale random fields (RSRF). For this purpose, input parameters are treated as spatially correlated random variables with assigned statistical attributes, while a vectorization strategy is used to reduce the computational cost of large-scale stochastic analyses. Deterministic simulations based on a hydro-mechanical model are then performed for multiple Monte Carlo realizations to compute maps of failure probability (pf). The methodology was applied to a well-documented series of rainfall-induced shallow landslides in a volcanic site for which field measurements were available to constrain the statistical variability of the hydraulic conductivity and treat this parameter as an RSRF. To analyze the results, four classes of landslide susceptibility characterized by different pf thresholds were used. Such classes were mapped over the study zone and throughout the storm event, allowing a direct comparison with the spatio-temporal evidence of landslide triggering. The results indicate that (i) uncertainty analyses neglecting the role of spatial correlation may lead to non-conservative estimates of landslide susceptibility and (ii) there is an interval of spatial correlation distance that optimizes the performance of the model, thus providing an indirect estimate of the heterogeneity of the site. Such results highlight the benefits of accounting for the uncertainty of the soil properties in regional-scale models and offer a new predictive stochastic framework to assess the implications of future rainfall scenarios over large areas.

Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean)

This work is focused on the landslide susceptibility assessment, applied to Mauritius Island. The study area is a volcanic island located in the western part of the Indian Ocean and it is characterized by a plateau-like morphology interrupted by three rugged mountain areas. The island is severely affected by geo-hydrological hazards, generally triggered by tropical storms and cyclones. The landslide susceptibility analysis was performed through an integrated approach based on morphometric analysis and preliminary Geographical Information System (GIS)-based techniques, supported by photogeological analysis and geomorphological field mapping. The analysis was completed following a mixed heuristic and statistical approach, integrated using GIS technology. This approach led to the identification of eight landslide controlling factors. Hence, each factor was evaluated by assigning appropriate expert-based weights and analyzed for the construction of thematic maps. Finally, all the collected data were mapped through a cartographic overlay process in order to realize a new zonation of landslide susceptibility. The resulting map was grouped into four landslide susceptibility classes: low, medium, high, and very high. This work provides a scientific basis that could be effectively applied in other tropical areas showing similar climatic and geomorphological features, in order to develop sustainable territorial planning, emergency management, and loss-reduction measures.

Analysis of Landslide Susceptibility of Belandingan Village, Songan A Village and Songan B Village of Kintamani, Bangli, Indonesia

The research conducted in order to find out how the susceptibility for landslides in the village of Belandingan, Songan A Village and Songan B Village. Scoring and overlay method used to determine the results of landslide susceptibility in the study area. Parameter used as the basis for overlay and scoring are soil types, rainfall data, structural geology, landform, slope and land use map. From the overlay result, 41 land unit were mapped on the research area. Based on scoring and GIS analysis, there are 4 classes of landslide susceptibility including not susceptible, low susceptibility, moderate susceptibility and high susceptibility. Most area of study area are are included as low susceptibility and medium susceptibility. Meanwhile high susceptibility mainly concentrated on the ancient caldera on west part of Batur Lake. Belandingan, Songan A and Songan B villages have various levels of landslide susceptibility, starting from this non-susceptible category, covering land units 34 and 39. Low susceptibility on land units 26, 29, 25, 23, 21, 38, 3, 12 and 11. While on land units 36, 30, 31, 32, 14, 15, 16, 17, 19, 4, 20, 40, 24, 6, 13, 2, 37, 7, 8, 9, 27.10, 22, 41, 1.5 and high susceptibility on land units 33, 18, 35, 28. The no susceptibility class mainly located in Songan A and Songan B Village with total area 94,405 Ha. Low susceptibility class are located mainly in Songan A Village with small amount in Songan B Village with total area 1196.768 Ha. Moderate susceptibility class located in all with total area 1866.346 Ha. High landslide susceptibility class located mainly in Songan B Village with small amount in Belandingan Village and Songan A Village with total area 339,979 Ha.

Landslide Susceptibility Mapping in Darjeeling Himalayas, India

Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.

Comparison of landslide susceptibility maps using random forest and multivariate adaptive regression spline models in combination with catchment map units

Landslide susceptibility mapping (LSM) is a critical tool for mitigating the damages caused by geologic disasters. The selection of map units and mathematical models greatly affects the efficiency of LSM. To obtain the most appropriate combination of map units and mathematical models, four scales of catchment map units (CMUs) were analyzed and random forest (RF) and multivariate adaptive regression spline (MARSpline) models were applied in Gero City, Japan. The percentage of correctly identified landslides and the areas under the relative operating characteristic (ROC) curve were used to evaluate the model performances. The results indicate that the RF model had higher prediction accuracy than the MARSpline model, especially when the size of the CMU was 0.09 km2. A relatively high percentage of landslides fell into the high and very high landslide susceptibility classes (73%) and the lowest percentage of landslides fell into the very low landslide susceptibility classes (0.82%). The prediction-area (P-A) plots indicated that the prediction rates were higher for the RF model than the MARSpline model. The results of this study also suggest that the model accuracy can be increased if the appropriate CMU size is used. Therefore, the potential benefits of using the RF model in combination with the appropriate CMU size should be further explored using additional landslide-conditioning factors and other models.

GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, N-E Algeria

Slope instability phenomena in Souk Ahras region are annually causing a great amount of damage mainly to road infrastructure, water main supply, and buildings. The main problem is that instabilities keep reoccurring despite the remedial measures brought about every time. The fact is there is not only a single factor that is behind these instabilities rather than the interplay of a large variety of factors pertaining to the geological, geomorphological, and hydrological characteristics of the terrain as well as human-related activities. Consequently, a spatial database of ten landslide-related factors were identified and used to assess landslide susceptibility and establish a model capable of predicting landslide prone areas. For this reason, three statistical methods are used for the landslide susceptibility assessment: logistic regression, frequency ratio, and weights of evidence in a GIS platform. A landslide inventory map was established from visual interpretation of satellite images and field survey data. Three landslide susceptibility maps were produced using different statistical models. Each susceptibility map subdivides the study area into five classes of landslide susceptibility: very low, low, moderate, high, and very high. These raster-based susceptibility maps were compared and verified with both training and validating inventory data. The area under the curve values, based on success rate, are between 82.11 and 90.57%, and those based on prediction rate are between 83.14 and 90.91%. The results showed that the logistic regression method is more consistent and reliable than the two other techniques, and it has the best performance among the three statistical methods.

A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil

Statistically based landslide susceptibility mapping has become an important research area in the last decades, and several bivariate and multivariate statistical approaches to landslide susceptibility assessments have been applied and compared in all regions of the world. The aim of this study was to compare different statistical approaches and to analyse the degree of spatial agreement between the landslide susceptibility maps produced. To this end, we selected seven statistical methods for comparison, namely, landslide density, likelihood ratio, information value, Bayesian model, weights of evidence, logistic regression and discriminant analysis, and then applied these to an inventory comprising 940 translational landslides, in the southeast region of Minas Gerais state in Brazil, at the western edge of the Quadrilatero Ferrifero (642.13 km2). In some statistical approaches, modifications were made to the input dependent variables. The landslides registered in the inventory map have been used in punctual and polygonal form. Six factors were considered as input landslide predisposing factors: slope angle, geomorphological units, slope curvature, lithological units, slope aspect and inverse wetness index. The combination order of the landslide predisposing factors was established based on a sensitivity analysis, which gave rise to five different cartographic combinations. In total, 58 statistical models of landslide susceptibility were produced, and the results were validated using success and prediction rate curves. The spatial agreement evaluation between the model results was carried out with kappa statistics. There were 214 comparisons of spatial agreement involving classified models at three relative degrees of susceptibility (high, medium and low landslide susceptibility classes). The results showed that all of the models so produced had satisfactory validation rates. The best landslide susceptibility models obtained areas under the curve of > 0.80 in the success and prediction rate curves, with emphasis on the weights of evidence, the information value and the likelihood ratio statistical methods. These statistical approaches were performed with the landslides mapped in the form of points. The landslide susceptibility classes of these models visually demonstrated a slightly more irregular spatial distribution when compared to the models performed with landslide polygons. The likelihood ratio model performed with landslide points presented one of the smallest areas for the high susceptibility class and the largest area for the low susceptibility class. The analysis of the spatial agreement showed that the models produced with a polygonal dependent variable tend to be more concordant, regardless of the statistical technique used. Moreover, we verified that spatial agreement tends to increase with increasing accuracy of the models. Despite the discrepancies found, most of the models compared showed a substantial or almost perfect degree of agreement.