Landslide Hazard Mapping Research Articles

Investigation of landslides and floods utilizing a geographic information system (A case study: Trenggalek Regency, East Java)

Trenggalek Regency is an area that is affected by floods and landslides every year. This study aimed to map areas that are prone to floods and landslides in each district using Geographic Information System (GIS) and Remote Sensing. In its processing, this disaster hazard map used secondary data sourced from the Shapefile Ina-Geoportal, soil type map, DEM data, BMKG data, Google Earth imagery, and BPS data for Trenggalek Regency which are then processed using ArcGis 10.7 software. These produced parameters for making flood and landslide hazard maps in the form of rainfall, slope, soil type, land use, and exposed population in Trenggalek Regency. After classifying, scoring, and overlaying the existing parameters, flood and landslide hazard maps were generated. The flood hazard map covered seven districts of the Trenggalek Regency, including Tugu, Karangan, Trenggalek, Pogalan, Gandusari, and a small part of Kampak are very prone to floods with an average of the districts in Trenggalek Regency have a result of fairly prone to floods. Meanwhile, the landslide hazard map shows that several districts such as Trenggalek, Watulimo, and Panggul have a high landslide hazard level, while the dominance of other areas is landslides with a moderate hazard level. With this processing, it can be seen that areas with high populations and residential areas are prone to floods or landslides.

Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh

AbstractLandslide hazards significantly affect economies and populations around the world, but locations where the greatest proportional losses occur are in data‐poor regions where capacity to estimate and prepare for these hazards is most limited. Earth observation (EO) data can fill key knowledge gaps, and can be rapidly used in settings with lower analytical capacity. In this study, we describe a novel series of methods designed to analyze landslide susceptibility, hazard and exposure in the region in and around the Rohingya refugee camps in Bangladesh, where limited data is juxtaposed with a major humanitarian crisis. We demonstrate that a high degree of accuracy is possible even when estimating susceptibility of relatively small landslides. In the context of this example, we also explore how estimates of landslide hazard and exposure are most beneficial to decisions made by humanitarian stakeholders relevant to natural hazards and risk. The unique opportunity to work alongside humanitarian end‐users has allowed us to produce focused products that can be tested while in development. In particular, we stress the importance of communicating the difference between a landslide “early warning system”—for which satellite data may be unsuitable at local scales—and a model that provides relative hazard estimates, where EO may be valuable. The toolbox of methods presented here could be used to generate landslide hazard and exposure maps in other data‐poor regions around the globe.

Landslide Hazard Assessment Map as an Element Supporting Spatial Planning: The Flysch Carpathians Region Study

Landslides and rock falls are among the many phenomena that have an impact on sustainable construction and infrastructure safety. The main causes of landslides are natural meteorological and hydrological phenomena. In building design and construction, environmental monitoring by identifying geotechnical hazards must be taken into account, as appropriate hazard assessment contributes to ensuring future construction safety. The Carpathian region in southern Poland is particularly predisposed to landslide formation. This may be favored by the nature of the shapes associated with the high and steep slopes of the region’s valleys. Another reason for concern is the flysch geological structure, which is characterized by alternating layers of water-permeable sandstones and poorly permeable shales, clays, and marls. Furthermore, the presence of a quaternary weathering cover makes the geological structure more susceptible to landslide processes and tectonic formations. The paper presents the results of a study whose aim was to elaborate a detailed landslide hazard map for a selected area in the Polish Carpathians, using statistical methods. The approach is based on the Hellwig method, which seems particularly useful in the assessment of susceptibility and landslide hazards on a local scale for a relatively small area. A two-stage study was conducted. The first stage of the research involved the creation of a database associated with environmental parameters and triggering factors, whereas the second stage consisted of the adoption of weights for seven thematic sections and their special features on the basis of expert knowledge. The hazard map developed as a result was compared to the mapping made using the weight-of-evidence method. The proposed data normalization method allows the use and analysis of both qualitative and quantitative data collected from various sources. The advantage of this method is the simple calculation procedure. A large-scale (1:2000) map might be used to assess the landslide hazard for specific cadastral units. Such a map becomes the basis for municipal spatial planning and may be able to influence investment decisions. Detailed landslide hazard maps are crucial for more precise risk evaluation for specific cadastral units. This, in turn, allows one to reduce serious economic and social losses, which might be the future results of landslides.

Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

Multi-Hazard Evaluation Using Cluster Analysis—For Designated Evacuation Centers of Yokohama

Hazard maps are usually prepared for each disaster, including seismic hazard maps, flood hazard maps, and landslide hazard maps. However, when the general public attempts to check their own disaster risk, most are likely not aware of the specific types of disaster. So, first of all, we need to know what kinds of hazards are important. However, the information that integrates multiple hazards is not well maintained, and there are few such studies. On the other hand, in Japan, a lot of hazard information is being released on the Internet. So, we summarized and assessed hazard data that can be accessed online regarding shelters (where evacuees live during disasters) and their catchments (areas assigned to each shelter) in Yokohama City, Kanagawa Prefecture. Based on the results, we investigated whether a grouping by cluster analysis would allow for multi-hazard assessment. We used four natural disasters (seismic, flood, tsunami, sediment disaster) and six parameters of other population and senior population. However, since the characteristics of the population and the senior population were almost the same, only population data was used in the final examination. From the cluster analysis, it was found that it is appropriate to group the designated evacuation centers in Yokohama City into six groups. In addition, each of the six groups was found to have explainable characteristics, confirming the effectiveness of multi-hazard creation using cluster analysis. For example, we divided, all hazards are low, both flood and Seismic hazards are high, sediment hazards are high, etc. In many Japanese cities, disaster prevention measures have been constructed in consideration of ground hazards, mainly for earthquake disasters. In this paper, we confirmed the consistency between the evaluation results of the multi-hazard evaluated here and the existing ground hazard map and examined the usefulness of the designated evacuation center. Finally, the validity was confirmed by comparing this result with the ground hazard based on the actual measurement by the past research. In places where the seismic hazard is large, the two are consistent with the fact that the easiness of shaking by actual measurement is also large.

Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: A case study of Tabriz, Iran

Due to the complexity of the causes of the sliding mass instabilities, landslide susceptibility and hazard evaluation are difficult, but they can be more carefully considered and regionally evaluated by using new programming technologies to minimize the hazard. This study aims to evaluate the landslide hazard zonation in the Tabriz region, Iran. A fuzzy logic-based multi-criteria decision-making method was proposed for susceptibility analysis and preparing the hazard zonation maps implemented in MATLAB programming language and Geographic Information System (GIS) environment. In this study, five main factors have been identified as triggering including climate (i.e., precipitation, temperature), geomorphology (i.e., slope gradient, slope aspect, land cover), tectonic and seismic parameters (i.e., tectonic lineament congestion, distribution of earthquakes, the unsafe radius of main faults, seismicity), geological and hydrological conditions (i.e., drainage patterns, hydraulic gradient, groundwater table depth, weathered geo-materials), and human activities (i.e., distance to roads, distance to the municipal areas) in the study area. The results of analyses are presented as a landslide hazard map which is classified into 5 different sensitive categories (i.e., insignificant to very high potential). Then, landslide susceptibility maps were prepared for the Tabriz region, which is categorized in a high-sensitive area located in the northern parts of the area. Based on these maps, the Bozgoosh-Sahand mountainous belt, Misho-Miro Mountains and western highlands of Jolfa have been delineated as risk-able zones.

Identifikasi Rawan Bencana Tanah Longsor Kabupaten Toraja Utara

North Toraja Regency is an area that is prone to landslides with a potential zone of medium to high ground movement, which has resulted in damage to land transportation routes between districts being cut off, damage to buildings, and loss of lives. The focus of this research is to determine the level of vulnerability and map the class of landslide hazards in the North Toraja Regency. This type of research is descriptive qualitative with Geographical Information Systems (GIS) approach and then analyzed by overlay and scoring (weighting). Based on data processing, the study produced a landslide hazard map in North Toraja Regency with five classes of vulnerability, namely: very low area of 58,049 km2 (50%), low area of 118,087 km2 (11.18%), medium area of 256,057 km2 (22.07%), rather high area of 195,872 km2 (16.07%), very high area of 11,972 km2 (1.03 %).

Mapping Landslide Hazard Risk Using Random Forest Algorithm in Guixi, Jiangxi, China

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

INTEGRATION OF GIS AND ADVANCED REMOTE SENSING TECHNIQUES FOR LANDSLIDE HAZARD ASSESSMENT: A CASE STUDY OF NORTHWEST SYRIA

Abstract. Landslide susceptibility and hazard mapping has been developed providing remarkable results through the integration of geographic information system (GIS) and remote sensing. In this regard, some approaches have considered the use of Sentinel-1 data and time-series interferometric synthetic aperture radar (InSAR) techniques, such as differential InSAR (D-InSAR) and persistent scatterers interferometric (PSI), for providing precise information about total amount and velocity of ground-surface deformations and landslides within a specific area during a specific time period which is important for disaster management’s planning process.In this paper, artificial neural network (ANN) was used as a statistical analysis method for landslide susceptibility mapping in Northwest Syria using multi-layer perceptron (MLP) neural network on a training dataset of one dependent variable (landslide or non-landslide) and nine independent variables (slope, aspect, curvature, land cover, NDVI, lithology, distance from faults, distance from road, distance from stream networks). The resulting map of landslide susceptibility was validated using area under curve (AUC) analysis using a testing dataset which showed 90.28% of AUC value. Then, landslide susceptibility map was reclassified into high-moderate-low classes and integrated with intensity map of mean velocity of ground-surface deformations during the time period form (16 October 2018) until (21 March 2019) by using a landslide hazard matrix in a GIS environment in order to get landslide hazard map of the study area for that time period. The result shows that around 44.4%, 52.9% and 2.5% of total study area was classified as a high, moderate and low hazard zone of landslide, respectively.

Assessment and Applicability Analysis of Dynamic Landslide Hazard Using Matrix Approach

The models to predict when and where a landslide occurs were constructed with different purposes and scopes, but tended to be complementary to each other. The objective of this study was to convert the static landslide hazard into the dynamic landslide hazard and combine a spatiotemporal hazard matrix to assess it, in order to enhance the usability of the early warning system for landslide and the landslide hazard map developed by the Korea Forest Service. We applied various matrix evaluation models to develop scenarios and assess the 2017 landslide occurred in Cheonan, Korea. The results of this study showed that a simple combination of matrices facilitated quantitative evaluation of the dynamic landslide hazard and its accuracy was higher than the static landslide hazard map. In addition, the dynamic landslide hazard can be assessed for the near future by integrating the 1-hour prediction data provided by the Korea Forest Service. The results of this study can help maximize the utilization of the existing national landslide forecast system and landslide hazard map, which should enhance the efficiency of managing landslide-prone zones during the rainy season.

Landslide Susceptibility Assessment Using an Optimized Group Method of Data Handling Model

Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present study, for the first time, the group method of data handling (GMDH) was used to generate landslide susceptibility map for a specific region in Uzbekistan. First, 210 landslide locations were identified by field survey and then divided randomly into model training and model validation datasets (70% and 30%, respectively). Data on nine conditioning factors, i.e., altitude, slope, aspect, topographic wetness index (TWI), length of slope (LS), valley depth, distance from roads, distance from rivers, and geology, were collected. Finally, the maps were validated using the testing dataset and receiver operating characteristic (ROC) curve analysis. The findings showed that the “optimized” GMDH model (i.e., using the gray wolf optimizer [GWO]) performed better than the standalone GMDH model, during both the training and testing phase. The accuracy of the GMDH–GWO model in the training and testing phases was 94% and 90%, compared to 85% and 82%, respectively, for the standard GMDH model. According to the GMDH–GWO model, the study area included very low, low, moderate, high, and very high landslide susceptibility areas, with proportions of 14.89%, 10.57%, 15.00%, 35.12%, and 24.43%, respectively.

Hazard Mapping of the Rainfall–Landslides Disaster Chain Based on GeoDetector and Bayesian Network Models in Shuicheng County, China

Landslides are among the most frequent natural hazards in the world. Rainfall is an important triggering factor for landslides and is responsible for topples, slides, and debris flows—three of the most important types of landslides. However, several previous relevant research studies covered general landslides and neglected the rainfall–topples–slides–debris flows disaster chain. Since landslide hazard mapping (LHM) is a critical tool for disaster prevention and mitigation, this study aimed to build a GeoDetector and Bayesian network (BN) model framework for LHM in Shuicheng County, China, to address these geohazards. The GeoDetector model will be used to screen factors, eliminate redundant information, and discuss the interaction between elements, while the BN model will be used for constructing a causality disaster chain network to determine the probability and risk level of the three types of landslides. The practicability of the BN model was confirmed by error rate and scoring rules validation. The prediction accuracy results were tested using overall accuracy, Matthews correlation coefficient, relative operating characteristics curve, and seed cell area index. The proposed framework is demonstrated to be sufficiently accurate to construct the complex LHM. In summary, the combination of the GeoDetector and BN model is very promising for spatial prediction of landslides.

Landslide Hazard Mapping in Panchase Mountain of Central Nepal

Landslide Hazard Mapping in Panchase Mountain of Central Nepal

Obtaining suitable logic-tree weights for probabilistic earthquake-induced landslide hazard analyses

In this study, an inventory of landslides induced by the 2011 Lorca earthquake (Mw 5.1) has been used in order to develop a new procedure to obtain objective logic-tree weights for a probabilistic earthquake-induced landslide hazard analysis. The 2011 Lorca earthquake triggered more than 250 landslides, mainly of disrupted type. The logic-tree was designed having regard to variability of relevant geotechnical parameters involved in the problem and uncertainties associated with the use of several empirical relationships in order to compute Newmark displacements. For the purpose, the resulting hazard maps were compared with this landslide inventory, and weights estimated for each branch of the logic tree based on these results. The best model for seismic landslide hazard mapping for a moderate earthquake correctly identifies around 72% of landslide areas. Based on the set of parameters that comprises (depth of failure surface, specific weight, cohesion, friction angle and Newmark displacement model), the corresponding weights were objectively established. These weights are reliable enough for the obtaining seismic landslide hazard maps and may be implemented in similar environments characterized by moderate-low magnitude earthquakes (Mw < 5.5).

Prediction and mapping of landslide hazard in North Sumatera

Mapping of landslide hazard in North Sumatera is very important to identify the areas potential. In this study we try to predict and mapping of landslide hazard in North Sumatera using scoring and GIS. Compare to the ground movement vulnerability and rainfall series, the areas with steep topography have landslide hazard forecasts, which are dominated by zones with medium hazard forecasts. High-level landslide hazard forecast maps are in the areas of South Tapanuli, Mandailing Natal, Humbang Hasundutan, Central Tapanuli, North Tapanuli, South Nias, West Nias, Padang Lawas, Toba Samosir, Langkat, Samosir, Pakpak Bharat, and Karo districts.

Investigation and dynamic analyses of rockslide-induced debris avalanche in Shuicheng, Guizhou, China

On July 23, 2019, a large catastrophic landslide occurred in Shuicheng, Guizhou, China. It was a rapid and long run-out landslide that resulted in more than 50 casualties. The displaced materials traveled an approximate distance of 1250 m, with a descending height of around 465 m. In this paper, seismic record analyses and numerical simulation were conducted to determine the run-out behavior of the Shuicheng landslide. To determine the main characteristics of the landslide, geological and climatic settings were obtained by conducting a detailed field survey. The DAN3D model was used to simulate the propagation and run-out process of the landslide. It was observed that a combined Frictional-Voellmy model provides assuring results in simulating the Shuicheng landslide. The total duration of the landslide is 60 s with an average velocity of 20 m/s. The maximum velocity is estimated to be 40 m/s along the right bank of the valley, while a high initial velocity of 24 m/s was recorded at the initiation of the landslide. Seismic records obtained from the nearby seismic stations were used to calculate the location of the event and interpret the dynamic process of the landslide. The Arias intensity, Hilbert-Huang transform, and Empirical mode decomposition were used to obtain a detailed time history of the landslide. The results of the numerical simulation and seismic record analyses are expected to portray the process of similar landslides. This will improve the accuracy of landslide hazard mapping in the region.

The geographical prerequisites for the identification and prevention of dangerous geomorphological processes in the mountain geosystems of the Alpine-Himalayan belt (on the example of the Major Caucasus of Azerbaijan)

Destructive natural phenomena are a serious, sometimes unsolvable, regional and local environmental and socioeconomic problem. This paper presents the results of a comprehensive analysis of materials from long-term geomorphological studies in the mountainous areas on the example of the Major Caucasus of Azerbaijan. The dangerous geomorphological processes on the example of the Major Caucasus of Azerbaijan were investigated in detail using large-scale maps, satellite imagery and aerial photography. Geomorphological maps were drawn (map of mudflow hazard and map of landslide hazard in the Azerbaijani part of the Major Caucasus). The research determined the dangerous zones where landslides could cover 65–70% of the total area and outlined the zones and regularities of spread of various types of mudflow origination sites. The analysis of the manifestations of most active (with catastrophic consequences) destructive natural processes and the morphotectonic structure of the studied area showed that the their occurrence and maximum intensity was confined to the weakest plexuses of mountains – intersections of faults and fractures of various directions and orders. A technique for assessing the eco-geomorphological risk to prevent dangerous natural phenomena was offered. The technique is based on the detection of zones with intensive geomorphological processes, which are often not dangerous separately, but could have catastrophic consequences together. The results obtained during the assessment of the effect of natural and man-caused factors on the stability of montane ecosystems may be used to forecast dangerous natural phenomena and to research geodynamical dangerous geomorphological process not only in Azerbaijan, but also in other regions of the Alpine-Himalayan orogenic belt. The obtained results can be used to plan and perform economic activities, determine and minimize the hazards and risks of occurrence of dangerous natural phenomena, and forecast such phenomena in the future.

Spatial Landslide Risk Assessment at Phuentsholing, Bhutan

Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extended to other areas around the investigated area. The tool can serve as an indicator for the civil authorities to analyze the risk posed by landslides due to the rapid infrastructure development in the region.

Spatial Analysis of Safety Factors due to Rain Infiltration in the Buyan-Beratan Ancient Mountains

Sukasada and Sawan Subdistricts are areas in Buleleng Regency that are often hit by landslides due to their geological conditions, since they are composed of weathered volcanic rocks, consisting of intersections with faults and rock joints. A rainfall model analysis is carried out in order to determine the probability of precipitation, while WindRose is used to analyze the duration of the dominant rainfall. Furthermore, SoilVision is used to determine the grain size distribution. Meanwhile, the equations proposed by van Genuchten and Fredlund and Xing are used to estimate the Soil-Water Characteristic Curve (SWCC). The Green-Ampt model is also used to calculate infiltration capacity. Slope stability is analyzed using an infinite slope model in order to produce a spatial-temporal prediction map of slope stability in the form of safety factor (SF). The results of the hydrology analysis reveal that rain with an intensity of 87.32-92.27 mm/day, a duration of 6-7 days, and types of soil in each study location in Sukasada and Sawan Subdistricts affects the amount of infiltration and it, in turn, affects slope stability. Extrapolation results for the spatial-temporal map of landslide hazard reveal the classification of safety factor as follows, SF &gt; 2.13 (low/stable), 1.40 &lt; SF &lt; 2.12 (moderate/critical) and SF &lt; 1.39 (high/landslide).

Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.