Shallow Landslide Susceptibility Research Articles

Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions

The inherent uncertainty in hydro-geotechnical parameters presents a significant challenge for accurately predicting rainfall-triggered shallow landslides in mountainous regions. In this study, a novel probabilistic framework was developed and implemented in the “Py.GIS-FSLAM-FORM” software, designed to address the complexities associated with parameter uncertainty, correlation, and distribution. By combining the Fast Shallow Landslide Assessment Model (FSLAM) with the First-Order Reliability Method (FORM), we have enhanced the traditional probabilistic approach to create more accurate landslide susceptibility maps. This study emphasizes the uncertainly of geotechnical parameters and the critical influence of hydrological conditions on landslide susceptibility, especially focusing on the interaction between antecedent recharge (qa) and event rainfall (Pe). In our study area (Val d’Aran, Spain), the probabilistically based results revealed that areas of very high susceptibility are significantly affected by event rainfall, particularly on slopes of 30–40 degrees and aspects between 100 and 250 degrees. The variability in geotechnical parameters, especially the coefficient of variation (COV) in cohesion and friction angle, plays a crucial role in landslide susceptibility assessment, with increased COVs leading to greater landslide uncertainty. Additionally, cross-negative correlations and non-normal distributions of geotechnical parameters substantially influence the spatial distribution of landslides, notably when combining antecedent recharge with event rainfall. These results highlight the importance of incorporating parameter variability and hydrological conditions in susceptibility models to improve the precision of regional landslide forecasts. While the study was performed in Val d'Aran, its methodologies and conclusions are relevant to mountainous areas worldwide, offering insights for refining landslide prediction models and susceptibility assessments, contributing to global efforts in landslide disaster prevention.

A holistic approach of remote sensing, GIS, and machine learning for shallow landslide susceptibility mapping in Gaganbawada region of Western Ghats, India

A holistic approach of remote sensing, GIS, and machine learning for shallow landslide susceptibility mapping in Gaganbawada region of Western Ghats, India

Comparing shallow landslide susceptibility maps in Northeastern Türkiye (Beşikdüzü, Trabzon): a multivariate statistical, machine learning, and physical data-based analysis

This study endeavors to assess and compare the efficacy of various modeling approaches, including statistical, machine learning, and physical-based models, in the creation of shallow landslide susceptibility maps within the Besikduzu district of Trabzon province, situated in the Black Sea Region of Türkiye. The landslide inventory data, spanning from 2000 to 2018, was acquired through meticulous field surveys and analysis of Google Earth satellite imagery. Key topographic and geologic input parameters, such as slope, aspect, topographic wetness index, stream power index, plan and profile curvature, and geologic units, were extracted from a high-resolution 10 m spatial DEM (Digital Elevation Model) and a 1:25,000 scaled digital geology map, respectively. Additionally, soil unit weight and shear strength parameters, critical for the physical-based model, were determined through field samples. To evaluate landslide susceptibility, logistic regression, random forest, and Shalstab were employed as the chosen methods. The accuracy of susceptibility maps generated by each method was assessed using the area under the curve method, yielding impressive values of 0.99 for the random forest model, 0.97 for the logistic regression model, and 0.93 for the Shalstab model. These results underscore the robust performance of all three methods, suggesting their applicability for generating shallow landslide susceptibility maps not only in the Black Sea Region but also in analogous areas with similar geological characteristics.

A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning

Machine learning has grown in popularity in the past few years for susceptibility and hazard mapping tasks. Necessary steps for the generation of a susceptibility or hazard map are repeatedly implemented in new studies. We present a Random Forest classifier-based landslide susceptibility and hazard mapping framework to facilitate future mapping studies using machine learning. The framework, as a piece of software, follows the FAIR paradigm, and hence is set up as a transparent, reproducible and modularly extensible workflow. It contains pre-implemented steps from conceptualisation to map generation, such as the generation of input datasets. The framework can be applied to different areas of interest using different environmental features and is also flexible in terms of the desired scale and resolution of the final map. To demonstrate the functionality and validity of the framework, and to explore the challenges and limitations of Random Forest-based susceptibility and hazard mapping, we apply the framework to a test case. This test case conveys the influence of the training dataset on the generated susceptibility maps in terms of feature combination, influence of non-landslide instances and representativeness of the training data with respect to the area of interest. A comparison of the test case results with the literature shows that the framework works reliably. Furthermore, the results obtained in this study complement the findings of previous studies that demonstrate the sensitivity of the training process to the training data, particularly in terms of its representativeness.

Quantification of climate change impact on rainfall-induced shallow landslide susceptibility: a case study in central Norway

ABSTRACT Climate change impact on rainfall-induced landslide susceptibility of a certain region is often implied based on expected changes in rainfall patterns and rarely explicitly quantified. This study aims to address this gap by implementing coupled landslide and climate modelling chains to explicitly assess the effects of changing rainfall patterns on rainfall-induced landslide susceptibility. The effects of climate change are integrated into the landslide modelling chain via Intensity-Duration-Frequency (IDF) curves for the present and future climate conditions for a landslide-prone study area located in central Norway. The effects of climate change on landslide susceptibility are examined by using a physical-based landslide prediction model with rainfall events of varying duration and intensity that are simulated based on the climate-dependent IDF curves. The novelty of this study is the proposition of a novel probabilistic framework to assess the climate change impact on landslide susceptibility for rainfall events with a given duration. The proposed framework accounts for both the uncertainties of rainfall events through probabilistic interpretation of IDF curves and the uncertainties in the landslide model with the Monte Carlo method. Compared to results based on only intense rainfall events, the proposed framework leads to a lower increase in the probability of landslide initiation and landslide-susceptible extents due to climate change.

Influence of Root Reinforcement on Shallow Landslide Distribution: A Case Study in Garfagnana (Northern Tuscany, Italy)

In this work, we evaluated the influence of root structure on shallow landslide distribution. Root density measurements were acquired in the field and the corresponding root cohesion was estimated. Data were acquired from 150 hillslope deposit trenches dug in areas either devoid or affected by shallow landslides within the Garfagnana Valley (northern Tuscany, Italy). Results highlighted a correlation between the root reinforcement and the location of measurement sites. Namely, lower root density was detected within shallow landslides, with respect to neighboring areas. Root area ratio (RAR) data allowed us to estimate root cohesion by the application of the revised version of the Wu and Waldron Model. Then, we propose a new method for the assimilation of the lateral root reinforcement into the infinite slope model and the limit equilibrium approach by introducing the equivalent root cohesion parameter. The results fall within the range of root cohesion values adopted in most of the physically based shallow landslide susceptibility models known in the literature (mean values ranging between ca. 2 and 3 kPa). Moreover, the results are in line with the scientific literature that has demonstrated the link between root mechanical properties, spatial variability of root reinforcement, and shallow landslide locations.

A Novel Heterogeneous Ensemble Framework Based on Machine Learning Models for Shallow Landslide Susceptibility Mapping

Landslides are devastating natural disasters that seriously threaten human life and property. Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management. Machine learning (ML) models are widely used in LSM but suffer from limitations such as overfitting and unreliable accuracy. To improve the classification performance of a single machine learning (ML) model, this study selects logistic regression (LR), support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT), and proposes a novel heterogeneous ensemble framework based on Bayesian optimization (BO), namely, stratified weighted averaging (SWA), to test its applicability in a typical landslide area in Yanbian Prefecture, China. Firstly, a dataset consisting of 1531 historical landslides was collected from field investigations and historical records, and a spatial database containing 16 predisposing factors was established. The dataset was divided into a training set and a test set in a ratio of 7:3. The results showed that SWA effectively improved the Accuracy, AUC, and robustness of the model compared to a single ML model. The SWA achieved the best classification results (Accuracy = 91.39% and AUC = 0.967). To verify the generalization ability of SWA, we selected published landslide datasets from Yanshan country and Yongxin country in China for testing. SWA also performed well, with an AUC of 0.871 and 0.860, respectively. As indicated by shapely values (SVs), Normalized Difference Vegetation Index (NDVI) is the factor that has the greatest impact on landslide occurrence. The landslide susceptibility maps obtained from this study will provide an effective reference program for land use planning and disaster prevention and mitigation projects in Yanbian Prefecture, China.

Predisposing Factors for Shallow Landslides in Alpine and Hilly/Apennines Environments: A Case Study from Piemonte, Italy

Landslides are the most common natural hazard in the Piemonte region (northwestern Italy). This study is focused on shallow landslides caused by the sliding of the surficial detrital-colluvial cover caused by rainfall and characterized by a sudden and fast evolution. This study investigates shallow landslide events compared with variables considered as main predisposing qualitative factors (lithology, pedology and land use) to obtain a zonation of shallow landslide susceptibility in a GIS environment. Additionally, wildfire occurrence is also evaluated as a further predisposing factor for shallow landslide initiation. The resulting susceptibility map shows a strong correlation between the first three variables and shallow landslide occurrence, while it shows a negligible, or very localized, relationship with wildfire occurrence. Through the intersection of the predisposing factors with the landslide data points, a map of homogeneous zones is obtained; each identified zone is characterized by uniform lithological, soil-type, and land-use characteristics. The shallow landslide density occurrence is computed for each zone, resulting in a four-range susceptibility map. The resulting susceptibility zones can be used to define and evaluate the hazard linked to shallow landslide events for civil protection and regional planning purposes.

Comparing physical and statistical landslide susceptibility models at the scale of individual trees

Physically or statistically based approaches are widely used to quantify shallow landslide susceptibility. Despite the underlying data, methods, and assumptions being significantly different, there has been little quantitative work to evaluate the differences in model outcomes. Therefore, we compare a previously developed physical and a statistical shallow landslide susceptibility model for a study area in New Zealand's silvopastoral hill country. Both models include individual tree effects on slope stability. To identify individual trees for both models, we use convolutional neural networks to delineate tree crowns and classify species at the scale of individual trees with regional aerial photography. SlideforMAP, a physically based probabilistic model that includes both lateral and basal root reinforcement, is adapted for application in silvopastoral landscapes. Root reinforcement models for physical modelling are subsequently developed for the most abundant tree classes based on allometric relationships to remotely sensed, above-ground metrics. In contrast, the statistical landslide susceptibility model uses binary logistic regression, including empirically derived individual tree influence models alongside topographic and lithological explanatory variables. Binary logistic regression displays a better performance (AUC of 0.911 and 0.936 vs. 0.871 and 0.826) for our study area, resulting from the model being trained and tested on an inventory of past shallow landslide observations, whereas SlideforMAP makes predictions largely independent of past slope failures. SlideforMAP has the advantage of computing shallow landslide susceptibility for different rainfall scenarios coupled with specific return periods. The two predictions of landslide susceptibility within the study area were > 70 % in agreement. Differences in the model predictions are due to the different assumptions and datasets used by the models. Important considerations for land management include recognizing the advantages and limitations of both approaches in relation to erosion control objectives. We argue that the contrast afforded by the two different approaches to slope stability modelling in terms of assumptions, methods, and spatial predictions can increase process understanding, since – while being different – both approaches are geomorphologically plausible. However, to increase performance of erosion and sediment mitigation, we suggest land managers prioritize tree planting in areas where the statistical and physical approach agree within the “high” susceptibility classification (8 % of the study area). Comparing shallow landslide susceptibility scenarios based on potential trees and a treeless baseline can further improve targeted planting.

Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability

This study aimed to develop a physical-based approach for predicting the spatial likelihood of shallow landslides at the regional scale in a transition zone with extreme topography. Shallow landslide susceptibility study in an area with diverse vegetation types as well as distinctive geographic factors (such as steep terrain, fractured rocks, and joints) that dominate the occurrence of shallow landslides is challenging. This article presents a novel methodology for comprehensively assessing shallow landslide susceptibility, taking into account both the positive and negative impacts of plants. This includes considering the positive effects of vegetation canopy interception and plant root reinforcement, as well as the negative effects of plant gravity loading and preferential flow of root systems. This approach was applied to simulate the regional-scale shallow landslide susceptibility in the Dadu River Basin, a transition zone with rapidly changing terrain, uplifting from the Sichuan Plain to the Qinghai–Tibet Plateau. The research findings suggest that: (1) The proposed methodology is effective and capable of assessing shallow landslide susceptibility in the study area; (2) the proposed model performs better than the traditional pseudo-static analysis method (TPSA) model, with 9.93% higher accuracy and 5.59% higher area under the curve; and (3) when the ratio of vegetation weight loads to unstable soil mass weight is high, an increase in vegetation biomass tends to be advantageous for slope stability. The study also mapped the spatial distribution of shallow landslide susceptibility in the study area, which can be used in disaster prevention, mitigation, and risk management.

Impact of forest type and age on shallow landslide susceptibility: a case study from the 2017 heavy rainfall in northern Kyushu, Japan

ABSTRACT Assessing the impact of forest type and age on shallow landslide susceptibility is important for managing protective functions of forests. Previous studies have examined the correlation between forest types and ages, and shallow landslide susceptibility, but causal effects of forest type and age on shallow landslide susceptibility are yet not clear. This study investigated the causal effect of forest type and age on shallow landslide susceptibility using a propensity score method by combining existing geo-data sets. Here, we focus on shallow landslides caused by an extreme rainfall event on 5–6 July 2017, in the mountainous areas of Asakura City and Toho Village, Fukuoka Prefecture, Japan. Randomly located samples of coniferous forest, broadleaved forest, and young forest exposed to shallow landslide events were analyzed. The inverse probability of treatment weighting with the propensity score was applied to the samples to compare the differences in shallow landslide susceptibility among the three forest classes. Young forest had increased shallow landslide susceptibility, compared with coniferous forest and broadleaved forest, by 3.70 × 10−2 and 4.12 × 10−2, respectively, which corresponded to a two to three times increase in shallow landslide susceptibility when the forest changes from coniferous or broadleaved forest to young forest. No significant difference in shallow landslide susceptibility between coniferous forest and broadleaved forest was observed. These differences in shallow landslide susceptibility should be considered when implementing forest management schemes.

Exploring the Potential of Portable Spectroscopic Techniques for the Biochemical Characterization of Roots in Shallow Landslides

In the present work, Raman, Fourier Transform Infrared (FTIR) and elemental Laser-Induced Breakdown Spectroscopy (LIBS) spectroscopic techniques were used for the assessment of the influence of plant root composition towards shallow landslide occurrence. For this purpose, analyses were directly carried out on root samples collected from chestnut forests of the Garfagnana basin (northern Apennines, Italy) in different areas devoid and affected by shallow landslides due to frequent heavy rain events. Results have highlighted a correlation between the biochemical constituents of wooden roots and the sampling areas. In particular, different content of lignin/cellulose, as well as minerals nutrients, have been detected in roots collected where shallow landslides occurred, with respect to more stable areas. The results achieved are in line with the scientific literature which has demonstrated the link between the chemical composition of roots with their mechanical properties and, in particular, tensile strength and cohesion. Finally, portable spectroscopic instrumentations were employed without the need for either any sample preparation for Raman and LIBS spectroscopy or minimal preparation for FTIR spectroscopy. This novel and fast approach has allowed achieving information on the content of the major constituents of the root cell, such as cellulose and lignin, as well as their mineral nutrients. This approach could be reasonably included among the vegetation protection actions towards instability, as well as for the evaluation of shallow landslide susceptibility, combining geological, vegetational and biochemical parameters with sustainability.

Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets

Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets

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.

Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method

Probabilistic analysis of rainfall-induced shallow landslide susceptibility using a physically based model and the bootstrap method

LANDSLIDE SUSCEPTIBILITY ASSESSMENT: SOIL MOISTURE MONITORING DATA PROCESSED BY AN AUTOMATIC PROCEDURE IN GIS FOR 3D DESCRIPTION OF THE SOIL SHEAR STRENGTH

Abstract. Slope stability is strongly influenced by soil hydraulic conditions. Considering rain-triggered shallow landslides, the stability can be markedly influenced by the propagation of the saturation front inside the unsaturated zone. Soil shear strength varies in the vadose zone depending on the type of soil and the variations of soil moisture. Monitoring of the unsaturated zone can be done by measuring volumetric water content using low-cost instrumentation, such as capacitive sensors that are easy to manage and provide data in near-real time. For a proper soil moisture assessment a laboratory soil-specific calibration of the sensors is recommended. Knowing the soil water content, the suction parameter can be estimated by a Water Retention Curve (WRC), and consequently the soil shear strength in unsaturated conditions is evaluated. Several models are already proposed for shallow landslide susceptibility evaluation, also in FOSS GIS environment. However, these models do not usually consider the soil shear strength in unsaturated conditions, even if it is crucial, especially in the case of shallow landslides. A procedure that allows the estimate of the soil shear strength starting from soil moisture monitoring data (from sensor networks or satellite-derived map) is here presented. Moreover, preliminary results relative to a case study (i.e. the landslide of Ceriana-Mainardo in Italy) are shown. The proposed procedure could be integrated into existing models for landslide susceptibility assessment and also for the emergency management.

Application of a Novel Hybrid Machine Learning Algorithm in Shallow Landslide Susceptibility Mapping in a Mountainous Area

Landslides can be a major challenge in mountainous areas that are influenced by climate and landscape changes. In this study, we propose a hybrid machine learning model based on a rotation forest (RoF) meta classifier and a random forest (RF) decision tree classifier called RoFRF for landslide prediction in a mountainous area near Kamyaran city, Kurdistan Province, Iran. We used 118 landslide locations and 25 conditioning factors from which their predictive usefulness was measured using the chi-square technique in a 10-fold cross-validation analysis. We used the sensitivity, specificity, accuracy, F1-measure, Kappa, and area under the receiver operating characteristic curve (AUC) to validate the performance of the proposed model compared to the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and RF models. The validation results demonstrated that the landslide susceptibility map produced by the hybrid model had the highest goodness-of-fit (AUC = 0.953) and higher prediction accuracy (AUC = 0.919) compared to the benchmark models. The hybrid RoFRF model proposed in this study can be used as a robust predictive model for landslide susceptibility mapping in the mountainous regions around the world.

Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning

Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The main purpose of this paper is to explore a comprehensive model with high reliability, accuracy, and intelligibility in LSA by combing statistical methods and ensemble learning techniques. Miyun country in Beijing is selected as the study area. Firstly, the dataset containing 370 landslide locations inventories and 13 conditioning factors were collected and non-landslide samples were prepared by clustering analysis. Secondly, random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting decision tree (Ada-DT) were selected as base learners for the Stacking ensemble method, and these methods were evaluated using measures like area under the curve (AUC). Finally, the Gini index and frequent ratio (FR) were combined to analyze the major conditioning factors. The results indicated that the performance of the Stacking method was enhanced with an AUC value of 0.944 while the basic classifiers also performed well with 0.906, 0.910, and 0.917 for RF, GBDT, and Ada-DT, respectively. Regions with a distance to a stream less than 2000 m, a distance to a road less than 3000 m, and elevation less than 600 m were susceptible to the landslide hazard. The conclusion demonstrates that the performance of LSA desires enhancement and the reliability and intelligibility of a model can be improved by combining binary and multivariate statistical methods.

Model-based assessment of shallow landslides susceptibility at a petrochemical site in Brazil

The Serra do Mar mountain range is the main site of shallow-landslides occurrence in Brazil. The application of physically-based models is an effective method to predict landslide susceptibility, which is of great importance in hazard assessments and urban-planning studies. Thus, the objective of this study is to compare landslide susceptibility scenarios created with SHALSTAB and SINMAP at two large watersheds (Mogi and Perequê) in Cubatão, Latin America’s largest petrochemical site. Model calibration is based on the landslide scars inventory of the 1985 event, the geotechnical parameters derived from soil samples and the topography sourced from a 5 m resolution DEM. Using the Receiver Operating Characteristics (ROC) analysis to assess model performance, SHALSTAB emerges as the best-fit model for both watersheds due to higher concentration of landslide scars in unstable areas (true positive results) and higher global accuracy. Even though SINMAP had similar degree of success, it was slightly less accurate and failed more often in the identification of potentially unstable areas. Comparative performance studies of physically-based models are fundamental to support effective and reliable hazard assessments in mountain regions, providing an outlook in how to proceed with more detailed studies.