Shallow Landslide Susceptibility Assessment Research Articles

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China

There is no doubt that land cover and climate changes have consequences on landslide activity, but it is still an open issue to assess and quantify their impacts. Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover (LULC) and climate. We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity. The future climate change factors were obtained from a 4-member multi-model ensemble that was derived from statistically downscaled regional climate simulations. The future LULC maps were simulated by the land change modeller (LCM) integrated into IDRISI Selva software. A total of six scenarios were defined by considering the rainfall (antecedent conditions and extreme events) and LULC changes towards two time periods (mid and late XXI century). A physically-based model was used to assess landslide susceptibility under these different scenarios. The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future. Under the scenario with a return period of 100 years, the antecedent rainfall in summer will increase by up to 63% whereas the event rainfall will increase by up to 54% for the late 21st century. The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land. The magnitude of this change can reach + 22.1% (forest) and –9.2% (farmland) from 2010 until 2100, respectively. We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change, leading to an over decrease in stability over the study area. This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change. Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.

Comparison of earthquake-induced shallow landslide susceptibility assessment based on two-category LR and KDE-MLR

Geological hazards caused by strong earthquakes have caused continuous social and economic losses and destruction of the ecological environment in the hazard area, and are mostly manifested in the areas with frequent occurrence of geological hazards or the clustering of geological hazards. Considering the long-term nature of earthquakes and geological disasters in this region, this paper takes ten earthquake-stricken areas in Wenchuan earthquake zone as examples to collect shallow landslide data in 2010, combined with the spatial location of landslides and other factors. Kernel density estimation (KDE) method is used to analyze the spatial characteristics of shallow landslide. Taking the space of shallow landslide as the characteristic variable and fully considering the regulating factors of earthquake-induced landslide: terrain complexity, distance to river, distance to fault, distance to road, lithology, normalized vegetation difference index (NDVI) and ground peak acceleration (PGA) as independent variables, based on KDE and polynomial logistic regression (MLR), A quantitative model of shallow landslide in the earthquake area is constructed. The results show that: (1) PGA has the greatest impact on landslide in the study area. (2) Compared with the two-category logistic regression (two-category LR) model, the susceptibility map of landslide prediction results based on the KDE-MLR landslide susceptibility prediction model is more consistent with the actual situation. (3) The prediction accuracy of the model validation set is 70.7%, indicating that the landslide susceptibility prediction model based on KDE-MLR can effectively highlight the spatial characteristics of shallow landslides in 10 extreme disaster areas. The research results can provide decision-making basis for shallow landslide warning and post-disaster reconstruction in earthquake-stricken areas.

Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest­–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability.

Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast

Statistical susceptibility assessment is a common approach applied worldwide for shallow landslide studies. Identification of morphological and geological conditions is essential and still incipient to evaluate the susceptibility of landslide events in the Brazilian territory. This study aimed to develop and compare shallow landslide susceptibility scenarios based on a bivariate statistical evaluation of geological (lithology and structures) and morphological (curvature, elevation, slope, and aspect) factors in Caraguatatuba, northern coast of Sao Paulo State in Brazil. A compilation of geological factors from published maps was made, and morphological maps were created based on Shuttle Radar Topography Mission (30 m). A bivariate statistical application by the informative value method was used to create four susceptibility scenarios, and the validation was achieved using the area under the curve (AUC). The results indicated that lithology was the more relevant conditioning factor, followed by elevation and slope. The methodology used to determine the susceptibility was efficient (AUC values between 0.809 and 0.841). The susceptibility scenario comparison identified that conditioning factors with the highest informational value generated the most accurate mapping. This indicates that using several conditioning factors does not necessarily generate a better map. This study contributes to shallow landslides research from a methodological perspective, as it is the first analysis of its kind in Serra do Mar Paulista, which are continuously affected by mass movements. Open-source data were chosen to be used, focusing on methodological applicability in other regions of the country, since resources for landslide studies in Brazil are low.

An approach based on socio-politically optimized neural computing network for predicting shallow landslide susceptibility at tropical areas

A new hybrid model approach based on Imperialist Competitive Algorithm, a socio-politically optimization, and neural computing networks (ICA-NeuralNet) was developed and proposed in this study with the aim is to improve the quality of the shallow landslide susceptibility assessment at the Ha Long city area, Quang Ninh province. This area, which belongs to one of the three key economic regions of Vietnam, has a high urbanization speed during the last ten years. However, the landslide has been a significant environmental hazard problem during the last five years due to extreme torrential rainstorms. For this regard, a geographic information system (GIS) database was established, which contains 170 landslide polygons that occurred during the last five years and ten influencing factors. The database was used for training and validating the ICA-NeuralNet model. The results showed that the integrated model achieves high performance with classification accuracy rates of 82.4% on the training dataset and 78.2% on the testing dataset. Therefore, the ICA-NeuralNet is subsequently employed for generating a landslide susceptibility map of the study area, which greatly supports the land-use planning as well as hazard mitigation/prevention of local authority.

Assessment of shallow landslide susceptibility using an artificial neural network

Landslides are one of the most common and damaging natural hazards in mountainous areas. However, due to the complexity of the mechanisms that trigger landslides, it is very difficult to predict exactly when a landslide will occur. Therefore, research on landslide prevention and mitigation has mainly focused on predicting the locations of unstable slopes that are prone to landslides and are affected by multiple external forces. The prediction of the spatial distribution of unstable slopes, termed landslide susceptibility zonation, is important for government land-use planning and for reducing unnecessary loss of life and property. In this study, we investigated unstable slopes and established a GIS- and artificial neural network- (ANN-) based method to predict areas of potential landslides in the Silurian stratum in the Enshi region in China. Using failure mechanism analysis of typical existing landslides in the Silurian stratum, an evaluation index system was created that represents the most relevant factors influencing slope stability; an ANN was used to develop a susceptibility model to predict the spatial distribution of unstable slopes prone to landslides. The results were validated by remote sensing data and field investigations. This research proves that the proposed method for predicting the location of unstable slopes based on intelligence theory and GIS technology is accurate and reliable.

Earthquake-Induced Shallow Landslide Susceptibility Assessment of Gorkha District

Nepal is very seriously affected by landslides every year causing loss of life and property. Large scale earthquakes that occurred in different time periods such as on 15th January, 1934 or that on 25th April 2015 have proved Nepal as seismically vulnerable -place. Nepal has witnessed several landslides during and after the earthquake events making some areas of land quite vulnerable for settlement and other usages. Therefore in order to minimize the impacts of landslides caused due to earthquakes, highly susceptible locations should be identified and spatial planning is made accordingly. Considering topographic effects in amplification of earthquake ground motion, Uchida et al. (2004) have developed a topographical parameter based empirical description of landslide susceptibility during an earthquake. In this research, the method proposed by Uchida et al. (2004) is utilized in raster GIS and landslide susceptibility analysis is performed in the study area of SulikotGaupalika of Gorkha district, Nepal which was severely hit by several landslides due to “Gorkha Earthquake 2015". The landslide inventory map of SulikotGaupalika due to “Gorkha Earthquake 2015" is obtained and is correlated with landslide susceptibility values as obtained by using Uchida et al. (2004). The analysis shows that the method proposed by Uchida et al. (2004) is more than 68.9% accurate in delineating the probable locations of earthquake induced landslides. By calibrating landslide data and landslide susceptibility values in a small site (i.e. SulikotGaupalika) within the study area, a final landslide susceptibility map is prepared for the whole study area of Gorkha district. The resultant susceptibility map is very useful for planning settlements, development activities and reconstruction planning.

Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

Correction to: Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map

The published version of this article, unfortunately, contained error. Fig. 8 correction was not carried out. Given in this article is the correct figure.

Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map

Shallow landslides (i.e., slide, flow, and complex) are widespread around the world, affecting the soil mantle and upper regolith as a result of the weathering of granitic bedrock, and periodically cause enormous social and economic damages. Shallow landslide hazards are predominantly due to the scarcity of warning signs during the pre-failure stage, high velocities reached in the post-failure phase, and an increase in mobilized volumes caused by the entrapment of material in the downhill path of the phenomena. Owing to the abovementioned aspects, susceptibility assessment of shallow landslides in weathered granitic rocks is a relevant issue for land use planning and design purposes. This study proposes a three-step methodology for the susceptibility assessment of these phenomena. The methodology has been tested and validated at the 1:10,000 scale over a 30.4-km2 area in southern Italy, where weathered granitic rocks are periodically affected by shallow landslides. This methodology is divided into three successive steps: step 1 consists of database creation, with an emphasis on the weathering grade map (including five weathering classes, from class II to class VI, each one characterized by comparable mechanical behavior), and steps 2 and 3 focus respectively on susceptibility map calibration and validation through statistical analyses. The area under the ROC curve (AUC) shows values ranging from 0.95 in step 2 (calibration) to 0.88 in step 3 (validation) and is a testament to the good overall predictive accuracy of the methodology. The obtained results demonstrate both the effectiveness and the consistency of the proposed methodology in performing susceptibility mapping of shallow landslides in weathered granitic rocks, as well as the important role played by the weathering grade map.

Susceptibility Assessment of Rainfall-induced Shallow Landslides using a GIS-supported Deterministic Analysis – A Pilot Study

The landscape of Guwahati city (Assam) is characterized by a number of residual hills (eighteen) scattered in and around the main urban areas. The geology of all these isolated hills is the same, comprising an overburden of soil (weathered mantle) over bedrocks of porphyritic granite, quartzo-feldspathic gneiss, migmatites and schistose rocks. Shallow landslides in the form of soil slips are regular phenomena in these hills during the monsoons which are triggered by spells of high intensity rainfall. The Narakasur hill which lies in the heart of the city was selected as a small sample area for deterministic landslide susceptibility analysis to forecast the spatial occurrence of rainfall-induced shallow landslides. A grid-based slope-stability analysis was incorporated with the GIS spatial functions adopting the “infinite-slope” geometry to balance the resisting and the driving forces acting on the sliding mass. Landslide susceptibility is expressed by Safety Factor.Input data for the analysis includes topographic slope, soil thickness, water table depth and material strength properties. The effect of Safety Factor values, arising due to variation in material properties and water table conditions, was shown over the study area. Safety factors were calculated for three different water table depth scenarios assuming that soil is completely dry, completely saturated with water and an intermediate condition. Based on the calculated values of the Safety Factor the area was divided into stable, critical and unstable zones.Comparing the results with the shallow landslide inventory map, it has been observed that more than 70% agreement between predicted shallow landslide susceptibility and the inventory.

Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method

The physically based model has been widely used in rainfall-induced shallow landslide susceptibility analysis because of its capacity to reproduce the physical processes governing landslide occurrence and a higher predictive capability. However, one of the difficulties in applying the physically based model is that uncertainties arising from spatial variability, measurement errors, and incomplete information apply to the input parameters and analysis procedure. Uncertainties have been recognized as an important cause of mismatch between predicted and observed distributions of landslide occurrence. Therefore, probabilistic analysis has been used to quantify the uncertainties. However, some uncertainties, because of incomplete information, cannot be managed satisfactorily using a probabilistic approach. Fuzzy set theory is applicable in this case. In this study, in order to handle uncertainty propagation through a physical model, fuzzy set theory, coupled with the vertex method and the point estimate method, was adopted for regional landslide susceptibility assessment. The proposed approach was used to evaluate susceptibility to rainfall-induced shallow landslides for a regional study area, and the analysis results were compared with landslide inventory to evaluate the performance of the proposed approach. The AUC values arising from the landslide susceptibility analyses using the proposed approach and probabilistic analysis were 0.734 and 0.736, respectively. However, when the COV values of the input parameters were reduced, the AUC values of the proposed approach and the probabilistic analysis were reduced to 0.722 and 0.688, respectively. It means that the performance of the fuzzy approach is similar to that of probabilistic analysis but is more robust against variation of input parameters. Thus, at catchment scale, the fuzzy approach can respond appropriately to the uncertainties inherent in physically based landslide susceptibility analysis, and is especially advantageous when the amount of quality data is very limited.

Shallow landslide susceptibility assessment using a novel hybrid intelligence approach

We present a hybrid intelligent approach based on Naive Bayes trees (NBT) and random subspace (RS) ensemble for landslide susceptibility mapping at the Bijar region, Kurdistan province (Iran). According to current literature, both NB and RS are machine learning techniques that have been rarely used for modeling of landslides. NBT is a relatively new decision trees-based algorithm in conjunction with Bayesian theories in building trees for classification, whereas RS is a relatively new ensemble framework with ability to improve performance of prediction models. In the hybrid approach, RS is used to generate subsets from the training data each subset is then used to construct a based classifier using NBT. For this purpose, a geospatial database for the study area was constructed that consisted of 111 landslide locations and 17 conditioning factors (slope degree, slope aspect, elevation above sea, curvature, profile curvature, plan curvature, stream power index, topographic wetness index, length-angle of slope, lithology, land use, distance to road, distance to fault, distance to stream, fault density, stream density, and rainfall). The database was used to construct and verify the proposed model. Performance of the model was evaluated using the receiver operating characteristics curve and area under the curve (AUC). The results showed that the proposed model performed well in this study (AUC = 0.886), and it improved significantly the performance of the NBT base classifier (AUC = 0.811). Overall, RS–NBT is promising which can be utilized for landslide susceptibility assessment in other landslide-prone areas.

Soil depth reconstruction for the assessment of the susceptibility to shallow landslides in fine-grained slopes

This paper proposes a methodology aimed at reconstructing the maximum thickness mobilized by shallow landslides in fine-grained soils with the aid of geological and geotechnical analyses. The methodology, implemented within a geographic information system (GIS) environment, is composed of two stages for map reconstruction and two stages for map validation. The first stage of map reconstruction is aimed at individuating the soil thickness on the basis of only topographical and geological analyses; the second stage improves the previously obtained map with the aid of morphological and geotechnical analyses that provide a thickness map usable for shallow landslide susceptibility assessment. This map is validated with the aid of both in situ investigations (stage I), and geotechnical models able to back-analyse shallow precipitation-induced landslides over a wide area (stage II). An application of the proposed methodology is provided for a test area of the Calabria region (southern Italy) that is representative of the Catanzaro Strait, where widely diffused shallow landslides in fine-grained soils systematically occur. The results highlight the usefulness and reliability of the geotechnical models when implemented with the aid of a database representative of fine-grained soils while a secondary role is played by in situ investigations that in the test site have been performed only in a few representative and accessible areas.

A multi-annual landslide inventory for the assessment of shallow landslide susceptibility – Two test cases in Vorarlberg, Austria

Geomorphological landslide inventories provide crucial input data for any study on the assessment of landslide susceptibility, hazard or risk. Several approaches for assessing landslide susceptibility have been proposed to identify areas particularly vulnerable to this natural hazard. What they have in common is the need for data of observed landslides. Therefore the first step of any study on landslide susceptibility is usually the compilation of a geomorphological landslide inventory using a geographical information system. Recent research has proved the feasibility of orthophoto interpretation for the preparation of an inventory aimed at the delineation of landslides with the use of distinctive signs in the imagery data. In this study a multi-annual landslide inventory focusing on shallow landslides (i.e. translational soil slides of 0–2m in depth) was compiled for two study areas in Vorarlberg (Austria) from the interpretation of nine orthophoto series. In addition, derivatives of two generations of airborne laser scanning data aided the mapping procedure. Landslide scar areas were delineated on the basis of a high-resolution differential digital terrain model. The derivation of landslide volumes, depths and depth-to-length ratios are discussed. Results show that most mapped landslides meet the definition of a shallow landslide. The inventory therefore provides the data basis for the assessment of shallow landslide susceptibility and allows for the application of various modelling techniques.

Shallow landslide susceptibility assessment in a semiarid environment — A Quaternary catchment of KwaZulu-Natal, South Africa

This paper presents an evaluation of landslide susceptibility maps based on the Frequency Ratio (FR) Landslide Susceptibility Index (LSI), logistic regression (LR), and artificial neural network (ANN) methods, applied within a Quaternary catchment in the KwaZulu-Natal region, South Africa. Landslide occurrence in this particular catchment is generally induced by heavy rainfall events where shallow landslips occur mostly on steep slopes thereby threatening informal housing at the base of slopes. The scope of this study is to compare the performance of landslide susceptibility models and rate the importance of landslide causal factors. Thirteen landslide causal factors, including altitude, slope angle, aspect, slope total curvature, slope plan curvature, slope profile curvature, proximity to stream network, Topographic Wetness Index (TWI), lithology, land cover, proximity to faults, proximity to dolerite sills and dykes, and proximity to the road network were considered in the study. Furthermore each causal factor was rated according to the above mentioned methods, and it was found that the most influential landslide causal factors are the, altitude, land cover, and lithology. The resulting susceptibility maps were classified into 5 classes ranging from Very low to Very high susceptibility. Model performance was tested using an independent validation set comprising 10% of all mapped landslides. For verification of the models' performance, receiver operating curves (ROCs) were calculated and the areas under curve (AUC) for the success rate curves were 0.736, 0.748, 0.827, and 0.809 for FR, LSI, LR and ANN respectively. The findings revealed that models showed promising results for shallow landslide susceptibility modelling since they all give accuracy values higher than 73%. The LR and ANN methods however proved to be superior in representing landslide susceptibility throughout the study area.

Assessment of shallow landslide susceptibility using the transient infiltration flow model and GIS-based probabilistic approach

This study proposes a probabilistic analysis method for modeling rainfall-induced shallow landslide susceptibility by combining a transient infiltration flow model and Monte Carlo simulations. The spatiotemporal change in pore water pressure over time caused by rainfall infiltration is one of the most important factors causing landslides. Therefore, the transient infiltration hydrogeological model was adopted to estimate the pore water pressure within the hill slope and to analyze landslide susceptibility. In addition, because of the inherent uncertainty and variability caused by complex geological conditions and the limited number of available soil samples over a large area, this study utilized probabilistic analysis based on Monte Carlo simulations to account for the variability in the input parameters. The analysis was performed in a geographic information system (GIS) environment because GIS can deal efficiently with a large volume of spatial data. To evaluate its effectiveness, the proposed analysis method was applied to a study area that had experienced a large number of landslides in July 2006. For the susceptibility analysis, a spatial database of input parameters and a landslide inventory map were constructed in a GIS environment. The results of the landslide susceptibility assessment were compared with the landslide inventory, and the proposed approach demonstrated good predictive performance. In addition, the probabilistic method exhibited better performance than the deterministic alternative. Thus, analysis methods that account for uncertainties in input parameters are more appropriate for analysis of an extensive area, for which uncertainties may significantly affect the predictions because of the large area and limited data.

Shallow landslide susceptibility assessment in a data-poor region of Guatemala (Comitancillo municipality)

Although landslides are frequent natural phenomena in mountainous regions, the lack of data in emerging countries is a significant issue in the assessment of shallow landslide susceptibility. A key factor in risk-mitigation strategies is the evaluation of deterministic physical models for hazard assessment in these data-poor regions. Given the lack of physical information, input parameters to these data-intensive deterministic models have to be estimated, which has a negative impact on the reliability of the assessment. To address this problem, we examined shallow landslide hazard in Comitancillo municipality, Guatemala. Shallow landslides are here defined as small (less than two or three metre-deep) rotational or translational slides or earth flows. We based our hazard simulation on the stability index mapping model. The model’s input parameters were estimated from a statistical analysis of factors affecting landslides in the municipality obtained from a geodatabase. The outputs from the model were analysed and compared to an inventory of small-scale landslides. The results of the comparison show the effectiveness of the method developed to estimate input parameters for a deterministic model, in regions where physical data related to the assessment of shallow landslide susceptibility is lacking.

Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225mm of cumulative rainfall in 7h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire variables were found to have a strong control on the occurrence of very rapid shallow landslides.

Susceptibility assessment of shallow landslides triggered by rainfall in tropical basins and mountainous terrains

This study presents the analyses and results of the implementation of the SHALSTAB model to study landslides caused by rainfall that occurred on September 21, 1990, in a basin of tropical and mountainous terrain in the Colombian Andes. In <3 h, 208 mm of precipitation fell within the study area, triggering more than 800 landslides. This event is unusual because of the huge number of landslides that took place. The results obtained by the model are compared to an inventory of landslides that occurred during the event, which shows a high overlap in spatial locations. Additionally, a receiver operating characteristics analysis shows that SHALSTAB is able to capture the physics involved in landslides triggered by rainfall in tropical and mountainous terrains; this suggests that the proposed model could be implemented successfully in a variety of similar tropical regions to identify slopes prone to failure due to rainfall with only a high-resolution digital elevation model and a few soil parameters .