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The accuracy of landslide susceptibility mapping is influenced by the quality of sample data, factor systems, and assessment methods. This study aims to enhance the representativeness and overall quality of the sample dataset through an effective sample expansion strategy, achieving greater precision and reliability in the landslide susceptibility model. An integrated interpretative framework for landslide susceptibility assessment is developed using the XGBoost-SHAP-PDP algorithm to deeply investigate the key contributing factors of landslides in karst areas. Firstly, 17 conditioning factors (e.g., surface deformation rate, land surface temperature, slope, lithology, and NDVI) were introduced based on field surveys, satellite imagery, and literature reviews, to construct a landslide susceptibility conditioning factor system in line with karst geomorphology characteristics. Secondly, a sample expansion strategy combining the frequency ratio (FR) with SBAS-InSAR interpretation results was proposed to optimize the landslide susceptibility assessment dataset. The XGBoost algorithm was then utilized to build the assessment model. Finally, the SHAP and PDP algorithms were applied to interpret the model, examining the primary contributing factors and their influence on landslides in karst areas from both global and single-factor perspectives. Results showed a significant improvement in model accuracy after sample expansion, with AUC values of 0.9579 and 0.9790 for the training and testing sets, respectively. The top three important factors were distance from mining sites, lithology, and NDVI, while land surface temperature, soil erosion modulus, and surface deformation rate also significantly contributed to landslide susceptibility. In summary, this paper provides an in-depth discussion of the effectiveness of LSM in predicting landslide occurrence in complex terrain environments. The reliability and accuracy of the landslide susceptibility assessment model were significantly improved by optimizing the sample dataset within the karst landscape region. In addition, the research results not only provide an essential reference for landslide prevention and control in the karst region of Southwest China and regional central engineering construction planning but also provide a scientific basis for the prevention and control of geologic hazards globally, showing a wide range of application prospects and practical significance.

This paper focuses on large-scale landslide susceptibility modelling in NW Croatia. The objective of this research was to provide new insight into stable and unstable area sampling strategies on a representative inventory of small and shallow landslides mainly occurring in soil and soft rock. Four strategies were tested for stable area sampling (random points, stable area polygon, stable polygon buffering and stable area centroid) in combination with four strategies for unstable area sampling (landslide polygon, smoothing digital terrain model derived landslide conditioning factors, polygon buffering and landslide centroid), resulting in eight sampling scenarios. Using Logistic Regression, Neural Network, Random Forest and Support Vector Machine algorithm, 32 models were derived and analysed. The main conclusions reveal that polygon sampling of unstable areas is an imperative in large-scale modelling, as well as that subjective and/or biased stable area sampling leads to misleading models. Moreover, Random Forest and Neural Network proved to be more favourable methods (0.804 and 0.805 AUC, respectively), but also showed extreme sensitivity to the tested sampling strategies. In the comprehensive comparison, the advantages and disadvantages of 32 derived models were analysed through quantitative and qualitative parameters to highlight their application to large-scale landslide zonation. The results yielded by this research are beneficial to the susceptibility modelling step in large-scale landslide susceptibility assessments as they enable the derivation of more reliable zonation maps applicable to spatial and urban planning systems.

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance.

Landslides are among the most relevant and potentially damaging natural risks, causing material and human losses. The department of Aube in France is well known for several major landslide occurrences. This study focuses on the assessment of Landslide Susceptibility (LS) using the Frequency Ratio (FR) as a statistical method, the Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) method, and Random Forest (RF) and k-Nearest Neighbor (kNN) as machine learning methods in the Aube department, northeast of France. Subsequently, the thematic layers of eight landslide causative factors, including distance to hydrography, density of quarries, elevation, slope, lithology, distance to roads, distance to faults, and rainfall, were generated in the geographic information system (GIS) environment. The thematic layers were integrated and processed to map landslide susceptibility in the study area. On the other hand, an inventory of landslides was carried out based on the database created by the French Geological Survey (BRGM), where 157 landslide occurrences were selected, and then RF and kNN models were trained to generate landslide maps (LSMs) of the study area. The generated maps were assessed by using the Area Under the Receiver Operating Characteristic Curve (ROC AUC). Subsequently, the accuracy assessment of the FR model revealed more accurate results (AUC = 66.0%) than AHP, outperforming the latter by 6%, while machine learning models results showed that RF gave better results than kNN (

Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models.

The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to landslides or demonstrate spatial biases, leading to differences in model predictions. To address the impact of non-landslide sample selection on landslide susceptibility predictions, this study uses the western region of Henan Province as a case study. Utilizing historical data, remote sensing interpretation, and field surveys, a sample dataset comprising 834 landslide points is obtained. Ten environmental factors, including elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road, are chosen to establish an evaluation index system. Negative sample sampling areas are delineated based on the susceptibility assessment outcomes derived from the information value model. Two sampling strategies, whole-region random sampling (I) and partition-based random sampling (II), are employed. Random Forest (RF) and Back Propagation Neural Network (BPNN) models are used to forecast and delineate landslide susceptibility in the western region of Henan Province, with prediction accuracy evaluated. The model prediction accuracy is ranked as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). Under the Receiver Operating Characteristic (AUC) curve and accuracy, the II-RF and II-BPNN models exhibit increases in the region by 12.17% and 15.61%, respectively, compared to the I-RF and I-BPNN models. Moreover, the II-BPNN model shows improvements over the I-BPNN model with increases in AUC and accuracy by 14.54% and 16.52%, respectively. This indicates enhancements in model performance and predictive capability. In terms of recall and specificity, the II-RF and II-BPNN models demonstrate increases in recall by 15.09% and 17.47%, respectively, and in specificity by 15.80% and 14.99%, respectively. These findings suggest that the optimized models have better predictive capabilities for identifying landslide and non-landslide areas, effectively reducing the uncertainty introduced by point data in landslide risk prediction.

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness.

Landslide susceptibility denotes the likelihood of a disaster event under specific conditions. The assessment of landslide susceptibility has transitioned from qualitative to quantitative methods. With the integration of information technology in geological hazard analysis, a range of quantitative models for assessing landslide susceptibility has emerged and is now widely used. To compare and evaluate the accuracy of these models, this study focuses on Xupu County in Hunan Province, applying several models, including the CF model, FR model, CF-LR coupled model, FR-LR coupled model, SVM model, and RF model, to assess regional landslide susceptibility. ROC curves are used to evaluate the reliability of the model’s predictions. The evaluation results reveal that the CF model (AUC = 0.756), FR model (AUC = 0.764), CF-LR model (AUC = 0.776), FR-LR model (AUC = 0.781), SVM model (AUC = 0.814), and RF model (AUC = 0.912) all have AUC values within the range of 0.7–0.9, indicating that the overall accuracy of the models is good and can provide a reference for landslide susceptibility zoning in the study area. Among these, the Random Forest model demonstrates the best accuracy for landslide susceptibility zoning in the study area. By extracting the extremely high susceptibility zones from the landslide susceptibility zonings obtained by six models, a comparative analysis of model adaptability was conducted. The results indicate that the Random Forest model has the best adaptability under specific conditions in Xupu County.

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production.

Landslides pose significant risks to human lives and infrastructure. The Medea region in Algeria is particularly susceptible to these destructive events, which result in substantial economic losses. Despite this vulnerability, a comprehensive landslide map for this region is lacking. This study aims to develop a novel hybrid metaheuristic model for the spatial prediction of landslide susceptibility in Medea, combining the Adaptive Neuro-Fuzzy Inference System (ANFIS) with four novel optimization algorithms (Genetic Algorithm—GA, Particle Swarm Optimization—PSO, Harris Hawks Optimization—HHO, and Salp Swarm Algorithm—SSA). The modeling phase was initiated by using a database comprising 160 landslide occurrences derived from Google Earth imagery; field surveys; and eight conditioning factors (lithology, slope, elevation, distance to stream, land cover, precipitation, slope aspect, and distance to road). Afterward, the Gamma Test (GT) method was used to optimize the selection of input variables. Subsequently, the optimal inputs were modeled using hybrid metaheuristic ANFIS techniques and their performance evaluated using four relevant statistical indicators. The comparative assessment demonstrated the superior predictive capabilities of the ANFIS-HHO model compared to the other models. These results facilitated the creation of an accurate susceptibility map, aiding land use managers and decision-makers in effectively mitigating landslide hazards in the study region and other similar ones across the world.

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.

Morinda officinalis, a traditional medicinal plant in southern China, has a well-established cultivation history in Zhaoqing and Yunfu City of the Guangdong Province, China, contributing significantly to the local economy. Inadequate cultivation practices of Morinda officinalis may heighten the risk of landslide occurrences due to its specific growth and harvesting characteristics. This issue presents a challenge to the sustainable advancement of agriculture and forestry in the area, underscoring the necessity for scholarly intervention to address and devise effective remedies. This research utilized the MaxEnt model to assess landslide susceptibility and habitat suitability for Morinda officinalis, aiming to delineate appropriate cultivation zones amidst changing climatic conditions. The findings indicate that the model demonstrated a high level of accuracy, achieving combined AUC values of 0.802 for landslide susceptibility and 0.861 for habitat suitability evaluations, meeting the criteria for classification as “highly accurate”. Regions such as the Yun’an District, Luoding City, and the Xinxing District in Yunfu City were identified as having a low landslide risk and being highly conducive to Morinda officinalis cultivation under current climate conditions. Future projections indicate an anticipated expansion of the species’ distribution area between 2021 and 2040 under different climate scenarios, with subsequent variations. Spatial analysis unveiled a notable trend in the research area, indicating greater suitability for cultivation in the southern region compared to the northern region. This suggests that Yunfu City holds promise for facilitating the cultivation of Morinda officinalis more effectively than Zhaoqing City. This research offers a practical approach for promoting the sustainable growth of the regional Morinda officinalis industry, while also serving as a valuable resource for other economic crops encountering comparable developmental obstacles.

This paper presents the results of landslide prediction modelling for young glacial areas performed using statistical methods. The area in question is urbanized and therefore mass wasting activity is a matter of interest to both the local community and the authorities. The analysis was based on the 2011 ‘Register of landslides and areas prone to mass movements with a scale of 1:10,000 for the city of Gdansk’ and the 2012 incomplete ‘Register of landslides and areas prone to mass movements with a scale of 1:10,000 for the city of Gdynia’. The research took into account geological, geomorphological, hydrological, hydrogeological, and anthropogenic conditions. The landslide susceptibility map was created using the statistical landslide index. The calculated indices were used to create a map of Gdansk’s landslide susceptibility. In Gdansk, 84.50% of the total diagnosed landslide area belongs to the high susceptibility class, 14.25% to the moderate susceptibility class, and only 1.25% to the low or very low susceptibility class. After extrapolation, the data was also used to create a susceptibility map for the remaining parts of the Tri-City area, Sopot and Gdynia. The difficulty of extrapolating landslide data for neighboring urban areas was indicated. In Gdansk, which had been covered by geological mapping, the best modelling results were obtained with a large number of causal factors. In Gdynia and Sopot, for which the statistical landslide index value was extrapolated from Gdansk, the best results were obtained when selected causal factors were considered. In Sopot and Gdynia, 81.6% of the landslide area belongs to the high susceptibility class, 15.1% to the moderate class, and 3.3% to the low susceptibility class. These results emphasize a different role of some causal factor classes in the occurrence of landslides in neighboring urban areas. The resultant maps show the areas in which mass wasting is the most probable in the future.

The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed.

The evaluation of landslide susceptibility plays a crucial role in preventing the risks associated with landslides and debris flows, providing valuable insights for the effective prevention and mitigation of geological hazards. However, there is limited research on high-altitude areas. Therefore, this study chose the western Tibetan Plateau as the study area, a representative area known for its susceptibility to landslides and high attitudes. In this study, seven factors were identified based on research objectives. Information value (IVM), weight of evidence (WOE), information value logistic regression (IVM-LR), weight of evidence logistic regression (WOE-LR), information value multi-layer perceptron (IVM-MLP) and weight of evidence multi-layer perceptron (WOE-MLP) were selected and compared for landslide susceptibility. The percentage of disaster area included in each risk level, the AUC value and the ROC curve were used to evaluate the accuracy of the results. The ROC curves of the results were close to the upper–left corner and the AUC values exceeded 0.85, an indication that all results were highly accurate. Moreover, the percentage of disaster area included for each risk showed an upward trend regarding susceptibility. The results indicated that the hybrid model exhibited superior performance in assessing landslide susceptibility at high altitudes. Overall, the results showed great significance regarding disaster prevention and mitigation measures of local governments.

The Chure Hills, already vulnerable due to their fragile nature, face increased landslide risk, prompting the need for reliable susceptibility assessment. This study uses Poisson regression modeling to assess landslide susceptibility in two highly susceptible districts of the Chure region. Variance inflation factor (VIF) tests were conducted to ensure robustness, indicating no multicollinearity among the variables. Subsequently, Poisson regression analysis identified eight significant variables, among which geology, lineament density, elevation, relief, slope, rainfall, solar radiance, and land cover types emerged as important factors associated with landslide count. The analysis revealed that higher lineament density and slope were associated with lower landslide counts, indicating potential stabilizing geological and topographical influences. The categorical variable, namely geology, revealed that middle Siwalik, upper Siwalik, and quaternary geological formations were associated with lower landslide counts than lower Siwalik. Land cover types, including areas under forest, shrubland, grassland, agricultural land, water bodies, and bare ground, had a substantial significant positive association with landslide count. The generated susceptibility map that exhibited a substantial portion (23.32% in Dang and 5.22% in Surkhet) of the study area fell within the very-high-susceptibility categories, indicating pronounced landslide susceptibility in the Dang and Surkhet districts of the Chure hills. This study offers valuable insights into landslide vulnerability in the Chure region, serving as a foundation for informed decision-making, disaster risk reduction strategies, and sustainable land-use and developmental policy planning.

In the frame of a regional study that is aimed at defining the landslide susceptibility in the upper Susa Valley (Western Italian Alps), this paper investigated the variation in the fracture network of rock masses in correspondence to faults zones. Fracture networks were characterized at sites located along transects that are oriented normally to fault traces and scattered in their adjacent sectors with the scope to be able to define and estimate the variation in fracture abundance in the bedrock. Fracture collection using a traditional geomechanical survey was combined with topological analysis to estimate the degree of fracturing of rock masses in terms of fracture intensity (P21) and connectivity. Several sites were selected, and the variation in fracturing intensity values and degree of connectivity allowed for the defining of “high fracturing zones” (HFZ) near fault traces moving from the background values of adjacent rock masses. Considering the variation in fracture intensity, a range of 400 m to 150 m HFZ can be defined, with considerable differences between the areas analysed. The values recorded show a high irregular variability in fracture intensity in correspondence to the mapped faults due to the interference and overlay of structures related to the complexity setting of the fault damage zone.

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.

Two of the eight main soil degradation processes with which soils worldwide are confronted are soil erosion and landslides. Specifically, landslides are a major threat in particular areas across Europe, often leading to serious impacts on population, property, and infrastructure. Regarding the abovementioned processes, the case study of the fatal Mandra flash flood (November 2017) in the Attica Region (Greece), which caused 24 deaths, and much infrastructure and building damage, is presented with the intention of assessing the relationship between soil erosion and landslide incidents. Investigations were executed from 2018 to 2022, and their outcomes were taken into consideration by the Technical Authority of the Attica Region. Soil erosion lines were delineated in a GIS and were validated using a previously generated regional Web-GIS landslide susceptibility map. The study presents soil erosion types from the Mandra fatal flash flood event and correlates them with already existing landslide susceptibility analyses for the Attica Region. The produced susceptibility map is a cartographic product on a regional scale (1:100,000) generated via a semiquantitative heuristic methodology named the Rock Engineering System (RES). The way in which both soil erodibility and landslide susceptibility maps were generated and validated could be the basis for proposing modeling approaches that can respond to new developments in European landslide policies.

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.

Landslides pose significant and serious geological threat disasters worldwide, threatening human lives and property; China is particularly susceptible to these disasters. This paper focuses on Pengyang County, which is situated in the Ningxia Hui Autonomous Region of China, an area prone to landslides. This study investigated the application of machine learning techniques for analyzing landslide susceptibility. To construct and validate the model, we initially compiled a landslide inventory comprising 972 historical landslides and an equivalent number of non-landslide sites (Data sourced from the Pengyang County Department of Natural Resources). To ensure an impartial evaluation, both the landslide and non-landslide datasets were randomly divided into two sets using a 70/30 ratio. Next, we extracted 15 landslide conditioning factors, including the slope angle, elevation, profile curvature, plan curvature, slope aspect, TWI (topographic wetness index), TPI (topographic position index), distance to roads and rivers, NDVI (normalized difference vegetation index), rainfall, land use, lithology, SPI (stream power index), and STI (sediment transport index), from the spatial database. Subsequently, a correlation analysis between the conditioning factors and landslide occurrences was conducted using the certainty factor (CF) method. Three landslide models were established by employing logistic regression (LR), functional trees (FTs), and random subspace functional trees (RSFTs) algorithms. The landslide susceptibility map was categorized into five levels: very low, low, medium, high, and very high susceptibility. Finally, the predictive capability of the three algorithms was assessed using the area under the receiver operating characteristic curve (AUC). The better the prediction, the higher the AUC value. The results indicate that all three models are predictive and practical, with only minor discrepancies in accuracy. The integrated model (RSFT) displayed the highest predictive performance, achieving an AUC value of 0.844 for the training dataset and 0.837 for the validation dataset. This was followed by the LR model (0.811 for the training dataset and 0.814 for the validation dataset) and the FT model (0.776 for the training dataset and 0.760 for the validation dataset). The proposed methods and resulting landslide susceptibility map can assist researchers and local authorities in making informed decisions for future geohazard prevention and mitigation. Furthermore, they will prove valuable and be useful for other regions with similar geological characteristics features.

Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments.

Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset’s suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis.

This work aims to analyse the mechanisms and factors contributing to shallow soil landslides in river valleys entrenched in lowlands on the example of the Central Radunia Valley. The combination of susceptibility analysis using geographic-information-system-based statistical models, field surveys, analysis of archival materials, and numerical modelling for the analysis of slope stability and hydrogeological processes allows for comprehensive landslide reconstruction, mass movement mechanism description, and an explanation of the role of triggering and causal factors. The results emphasise the need for cross-disciplinary studies of shallow soil landslides. The identification and prioritisation of the causal factors indicate that geomorphological conditions play a particularly important role. The current study shows that the greatest influence on landslide formation in the Central Radunia Valley is slope angle, as determined using a high-resolution digital elevation model. The slope angle factor is sufficient to produce a reliable susceptibility map (the areas under the curve of the success rate and prediction rate curves are 87.84% and 85.34%, respectively). However, numerical modelling of slope failure also clearly indicated that there was a significant influence of anthropogenic impacts on the landslide process. We determined that the main triggering factor causing the January 2019 Rutki landslide was related to the drilling of a borehole on 10 January 2019. The water used for drilling hydrated the soil and thus weakened the stability conditions.

Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (115 mm), or a shorter DTRD (

In statistical landslide susceptibility evaluation, the quality of the model and its prediction image heavily depends on the quality of the landslide inventories used for calibration. However, regional-scale inventories made available by public territorial administrations are typically affected by an unknown grade of incompleteness and mapping inaccuracy. In this research, a procedure is proposed for verifying and solving such limits by applying a two-step susceptibility modeling procedure. In the Torto River basin (central-northern Sicily, Italy), using an available regional landslide inventory (267 slide and 78 flow cases), two SUFRA_1 models were first prepared and used to assign a landslide susceptibility level to each slope unit (SLU) in which the study area was partitioned. For each of the four susceptibility classes that were obtained, 30% of the mapping units were randomly selected and their stable/unstable status was checked by remote analysis. The new, increased inventories were finally used to recalibrate two SUFRA_2 models. The prediction skills of the SUFRA_1 and SUFRA_2 models were then compared by testing their accuracy in matching landslide distribution in a test sub-basin where a high-resolution systematic inventory had been prepared. According to the results, the strong limits of the SUFRA_1 models (sensitivity: 0.67 and 0.57 for slide and flow, respectively) were largely solved by the SUFRA_2 model (sensitivity: 1 for both slide and flow), suggesting the proposed procedure as a possibly suitable modeling strategy for regional susceptibility studies.

Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE) model with random forest (RF) and support vector machine (SVM) models. An inventory of 1723 rainfall-induced landslides was generated and randomly selected for training (1199; 70%) and validation (524; 30%) purposes. A set of 14 relevant factors was selected and checked for multicollinearity. These factors were first ranked using Information Gain and Chi-square feature ranking algorithms. Furthermore, Wilcoxon Signed Rank Test and One-Sample T-Test were applied to check their statistical significance. An optimum subset of 11 landslide causative factors was then used for generating landslide susceptibility maps (LSM) using hybrid SE-RF and SE-SVM models. These LSM’s were validated and compared using receiver operating characteristic (ROC) curves and performance matrices. The SE-RF performed better with training and validation accuracies of 96.93% and 88.94%, respectively, compared with the SE-SVM model with training and validation accuracies of 94.05% and 82.4%, respectively. The prediction matrices also confirmed that the SE-RF model is better and is recommended for the landslide susceptibility analysis of similar mountainous regions worldwide.

The determination of susceptibility to rainfall-induced landslides is crucial in developing a robust Landslide Early Warning System (LEWS). With the potential uncertainty of susceptibility changes in mountain environments due to different precipitation thresholds related to climate change, it becomes important to evaluate these changes. In this study, we employed a machine learning approach (logistic models) to assess susceptibility changes to landslides in the Central Andes. We integrated geomorphological features such as slope and slope curvature, and precipitation data on different days before the landslide. We then split the data into a calibration and validation database in a 50/50% ratio, respectively. The results showed an area under the curve (AUC) performance of over 0.790, indicating the model’s capacity to represent prone-landslide changes based on geomorphological and precipitation antecedents. We further evaluated susceptibility changes using different precipitation scenarios by integrating Intensity/Duration/Frequency (IDF) products based on CHIRPS data. We concluded that this methodology could be implemented as a Rainfall-Induced Landslides Early Warning System (RILEWS) to forecast RIL occurrence zones and constrain precipitation thresholds. Our study estimates that half of the basin area in the study zone showed a 59% landslide probability for a return of two years at four hours. Given the extent and high population in the area, authorities must increase monitoring over unstable slopes or generate landslide early warning at an operational scale to improve risk management. We encourage decision-makers to focus on better understanding and analysing short-duration extreme events, and future urbanization and public infrastructure designs must consider RIL impact.

Landslides are common and highly destructive geological hazards that pose significant threats to both human lives and property on a global scale every year. In this study, a novel ensemble broad learning system (BLS) was proposed for evaluating landslide susceptibility in Taiyuan City, Northern China. Meanwhile, ensemble learning models based on the classification and regression tree (CART) and support vector machine (SVM) algorithms were applied for a comparison with the BLS-AdaBoost model. Firstly, in this study, a grand total of 114 landslide locations were identified, which were randomly divided into two parts, namely 70% for model training and the remaining 30% for model validation. Twelve landslide conditioning factors were selected for mapping landslide susceptibility. Subsequently, three models, namely CART-AdaBoost, SVM-AdaBoost and BLS-AdaBoost, were constructed and used to map landslide susceptibility. The frequency ratio (FR) was used to assess the relationship between landslides and different influencing factors. Finally, the three models were validated and compared on the basis of both statistical-based evaluations and ROC curve-based evaluations. The results showed that the integrated model with BLS as the base learner achieved the highest AUC value of 0.889, followed by the integrated models that used CART (AUC = 0.873) and SVM (AUC = 0.846) as the base learners. In general, the BLS-based integrated learning methods are effective for evaluating landslide susceptibility. Currently, the application of BLS and the integrated BLS model for evaluating landslide susceptibility is limited. This study is one of the first efforts to use BLS and the integrated BLS model for evaluating landslide susceptibility. BLS and its improvements have the potential to provide a more powerful approach to assess landslide susceptibility.

The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex information of landslide evaluation factors, and low prediction accuracy of landslide susceptibility, a landslide susceptibility evaluation method based on the deep attention dilated residual convolutional neural network (DADRCNN) is proposed. First, the dilated convolution unit (DCU) is used to increase the network receptive field, aggregate multi-scale information, and enhance the model ability to capture the characteristics of landslide evaluation factors. Second, the deep residual module (DRM) is used to solve the issue of gradient disappearance and better extract data features by overlaying the residual function mapping layer and increasing the network depth. Finally, the channel attention residual module (CARM) is introduced to learn the varying importance of different landslide evaluation factors, and assign different weights to improve the susceptibility prediction accuracy. The experimental results show that the DADRCNN method can extract features around the sample points, expand the receptive field, and deeply mine the information. It mitigates the lack of sample information in training, focuses on important feature information, and significantly improves the prediction accuracy.

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM, but no consensus exists on which model is most suitable or best. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost), and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong County, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (sensitivity = 79.38%, specificity = 91.00%, accuracy = 85.28%, and AUC = 0.908), while the LR model performed the worst (sensitivity = 79.38%, specificity = 76.00%, accuracy = 77.66%, and AUC = 0.838) and the CatBoost model performed better (sensitivity = 76.28%, specificity = 85.00%, accuracy = 80.81%, and AUC = 0.893). Moreover, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, a more detailed analysis of conditioning factors was conducted, but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

In July 2015, more than 100 landslides caused by Cyclone Komen resulted in damage to approximately 1000 buildings in the mountainous region of Hakha City, Myanmar. This study aimed to identify potential landslide susceptibility for newly developed resettlement areas in Hakha City before and after urbanization. The study evaluated landslide susceptibility through statistical modeling and compared the level of susceptibility before and after urbanization in the region. The information value model was used to predict landslide susceptibility before and after urbanization, using 10 parameter maps as independent variables and 1 landslide inventory map as the dependent variable. Four landslide types were identified in the study area: shallow earth slide, deep slide, earth slump, and debris flow. Susceptibility analyses were conducted separately for each type to better recognize the different aspects of landslide susceptibility in planned urban areas. By comparing the results of the susceptibility index before and after urbanization, suitable urban areas with lower landslide susceptibility could be identified. The results showed that high-potential landslide susceptibility increased by 10%, 16%, and 5% after urbanization compared with before urbanization in three Town Plans, respectively. Therefore, Town Plan 3 is selected as the most suitable location for the resettlement area in terms of low risk of landslides.

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.

Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model.

Landslides can cause severe damage to both the environment and society, and many statistical, index-based, and inventory-based methods have been developed to assess landslide susceptibility; however, it is still challenging to choose the most effective method and properly identify major driving factors for specific regions. Here, we applied four machine learning algorithms, adaptive boosting (AdaBoost), gradient-boosting decision tree (GBDT), multilayer perceptron (MLP), and random forest (RF), to predict the landslide susceptibility at 30 m spatial scale based on thirteen landslide conditioning factors (LCFs) in a landslide-vulnerable region. Based on inventory landslide points, the classification results were evaluated, and indicated that the performance of the RF (F1-score: 0.85, AUC: 0.92), AdaBoost (F1-score: 0.83, AUC: 0.91), and GBDT (F1-score: 0.83, AUC: 0.88) methods were significantly better than the MLP (F1-score: 0.76, AUC: 0.79) method. The results further indicated that the areas with high and very high landslide risk (susceptibility greater than 0.5) accounted for about 40% of the study region. All four models matched well and predicted similar spatial distribution patterns in landslide susceptibility, with the very high risk areas mostly distributed in the western and southeastern regions. Daoshi, Qingliangfeng, Jinnan, and Linglong towns have the highest landslide risk, with mean susceptibility levels greater than 0.5. The leading contributing factors to landslide susceptibility were slightly different for the four models; however, population density, distance to road, and relief amplitude were generally among the top leading factors for most towns. Our study provided significant information on the highly landslide-prone areas and the major contributing factors for decision-makers and policy planners, and suggested that different areas should take unique precautions to mitigate or avoid severe damage from landslide events.

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation.

A landslide is one of the most destructive natural disasters in the world. The accurate modeling and prediction of landslide hazards have been used as some of the vital tools for landslide disaster prevention and control. The purpose of this study was to explore the application of coupling models in landslide susceptibility evaluation. This paper used Weixin County as the research object. First, according to the landslide catalog database constructed, there were 345 landslides in the study area. Twelve environmental factors were selected, including terrain (elevation, slope, slope direction, plane curvature, and profile curvature), geological structure (stratigraphic lithology and distance from fault zone), meteorological hydrology (average annual rainfall and distance to rivers), and land cover (NDVI, land use, and distance to roads). Then, a single model (logistic regression, support vector machine, and random forest) and a coupled model (IV–LR, IV–SVM, IV–RF, FR–LR, FR–SVM, and FR–RF) based on information volume and frequency ratio were constructed, and the accuracy and reliability of the models were compared and analyzed. Finally, the influence of environmental factors on landslide susceptibility under the optimal model was discussed. The results showed that the prediction accuracy of the nine models ranged from 75.2% (LR model) to 94.9% (FR–RF model), and the coupling accuracy was generally higher than that of the single model. Therefore, the coupling model could improve the prediction accuracy of the model to a certain extent. The FR–RF coupling model had the highest accuracy. Under the optimal model FR–RF, distance from the road, NDVI, and land use were the three most important environmental factors, ac-counting for 20.15%, 13.37%, and 9.69%, respectively. Therefore, it was necessary for Weixin County to strengthen the monitoring of mountains near roads and areas with sparse vegetation to prevent landslides caused by human activities and rainfall.

Landslide impact is potentially hazardous to an urban environment. Landslides occur at certain slope levels over time and require practical slope analysis to assess the nature of the slope where a landslide is likely to occur. Thus, acquiring very high-resolution remote sensing data plays a significant role in determining the slope surface. For this study, 12 landslide conditioning parameters with 10 × 10 cell sizes that have never been previously collectively applied were created. These factors were created directly from the LiDAR (Light Detection and Ranging) DEM (digital elevation model)using their layer toolboxes, which include slope, aspect, elevation, curvature, and hill shade. Stream power index (SPI), topographic wetness index (TWI), and terrain roughness index (TRI) were created from spatial layers such as slope, flow direction, and flow accumulation. Shapefiles of distances to roads, lakes, trees, and build-up were digitized as land use/cover from the LiDAR image and produced using the Euclidean distance method in ArcGIS. The parameters were selected based on expert knowledge, previous landslide literature, and the study area characteristics. Moreover, multicriteria decision-making analysis, which includes the analytic hierarchy process (AHP) and fuzzy logic approaches not previously utilized with a LiDAR DEM, was used in this study to predict the possibility of a landslide. The receiver operating characteristics (ROC) were used for the validation of results. The area under the curve (AUC) values obtained from the ROC method for the AHP and fuzzy were 0.859 and 0.802, respectively. The final susceptibility results will be helpful to urban developers in Malaysia and for sustainable landslide hazard mitigation.

Though danger prediction and countermeasures for landslides are important, it is fundamentally difficult to take preventive measures in all areas susceptible to dangerous landslides. Therefore, it is necessary to perform landslide susceptibility mapping, extract slopes with high landslide hazard/risk, and prioritize locations for conducting investigations and countermeasures. In this study, landslide susceptibility mapping along the whole slope of the Japanese archipelago was performed using the analytical hierarchy process (AHP) method, and geographic information system analysis was conducted to extract the slope that had the same level of hazard/risk as areas where landslides occurred in the past, based on the ancient landslide topography in the Japanese archipelago. The evaluation factors used were elevation, slope angle, slope type, flow accumulation, geology, and vegetation. The landslide susceptibility of the slope was evaluated using the score accumulation from the AHP method for these evaluation factors. Based on the landslide susceptibility level (I to V), a landslide susceptibility map was prepared, and landslide susceptibility assessment in the Japanese archipelago was identified. The obtained landslide susceptibility map showed good correspondence with the landslide distribution, and correlated well with past landslide occurrences. This suggests that our method can be applied to the extraction of unstable slopes, and is effective for prioritizing and implementing preventative measures.

Landslides are one of the most prevalent environmental disasters in the Hengduan Mountain Region. Landslides lead to severe economic damage and property loss, as well as fatalities. Furthermore, they tend to increase in the context of climate change. The purpose of this study is to comprehensively assess landslide susceptibility across the Hengduan Mountain Region in southwest China. Specifically, the analysis is focused on the eastern boundary of the Tibetan Plateau within the context of future climate change scenarios, which are based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model ensemble. The Generalized Additive Model (GAM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) were selected in order to map landslide susceptibility within the context of 1.5–4.0 °C warming scenarios. This was achieved by considering the changes in extreme rainfall that exceeded the landslide triggering thresholds. The results show that the frequency over extreme rainfall thresholds (FOERT) tend to increase in conjunction with warming targets, thereby ranging from 2.3/a (at a 1.5 °C warming) to 9.0/a (at a 4.0 °C warming) on average. Such elevated extreme precipitation events contribute to an increase in projected future zones of high landslide susceptibility when compared to the historical baseline period ranging from −1.2% (at a 1.5 °C warming) to 4.0% (at a 4.0 °C warming) using different machine learning models. Moreover, the extent of high susceptibility zones increases more significantly in the context of 4.0 °C warming when compared to the historical baseline results. These results indicate the importance of limiting the global temperature rise to 1.5 as well as 2 °C. The high landslide susceptibility zones estimated by the CMIP6 multi-models ensemble are mainly located in the central and southeastern regions of the Hengduan Mountain Region. The possible changes in terms of introducing extreme precipitation in order to assess landslide susceptibility in the context of climate change that is proposed in this study may be further applied to additional study areas. These projections under different targets can provide scientific guidelines for the purposes of the development of climate change adaptation strategies.

Landslide susceptibility evaluation can accurately predict the spatial distribution of potential landslides, which offers great usefulness for disaster prevention, disaster reduction, and land resource management. Aiming at the problems of insufficient samples for landslide compilation, difficulty in expanding landslide samples, and insufficient expression of nonlinear relationships among evaluation factors, this paper proposes a new evaluation method of landslide susceptibility combining deep autoencoder and multi-scale residual network (DAE-MRCNN). In the first step, a deep autoencoder network was used to learn the feature expression of the original landslide data in order to acquire effective features in the data. Next, a multi-scale residual network was constructed; specifically, the model was improved into a deep residual network model by adding skip connections in the convolutional layer. In addition, the multi-scale idea was utilized to fully extract the scale characteristics of the evaluation factors. Finally, the model was used for feature training, and the results were input into the Softmax classifier to complete the prediction of landslide susceptibility. For this purpose, a machine learning method and two state-of-the-art deep learning methods, namely SVM, CPCNN-ML, and 2D-CNN, were utilized to model landslide susceptibility in Hanzhong City, Shaanxi Province. The proposed method produced the highest model performance of 0.891, followed by 0.842, 0.869, and 0.873. The experimental results show that the DAE-MRCNN method can fully express the complex nonlinear relationships among the evaluation factors, alleviate the problem of insufficient samples in convolutional neural networks (CNN) training, and significantly improve the accuracy of susceptibility prediction.

Landslide susceptibility modelling in tropical climates is hindered by incomplete inventory due to rapid development and natural processes that obliterate field evidence, making validation a challenge. Susceptibility modelling was conducted in Kuala Lumpur, Malaysia using a new spatial partitioning technique for cross-validation. This involved a series of two alternating east-west linear zones, where the first zone served as the training dataset and the second zone was the test dataset, and vice versa. The results show that the susceptibility models have good compatibility with the selected landslide conditioning factors and high predictive accuracy. The model with the highest area under curve (AUC) values (SRC = 0.92, PRC = 0.90) was submitted to the City Council of Kuala Lumpur for land use planning and development control. Rainfall-induced landslides are prominent within the study area, especially during the monsoon period. An extreme rainfall event in December 2021 that triggered 122 landslides provided an opportunity to conduct retrospective validation of the model; the high predictive capability (AUC of PRC = 0.93) was reaffirmed. The findings proved that retrospective validation is vital for landslide susceptibility modelling, especially where the inventory is not of the best quality. This is to encourage wider usage and acceptance among end users, especially decision-makers in cities, to support disaster risk management in a changing climate.

Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and resolution/precision trade-off) and purely geomorphological (the representativeness of the mapping units and the diagnostic areas) to cartographic (the suitability of the obtained prediction images for the final users) topics. In this paper, the results of landslide susceptibility modelling in a 343 km2 catchment for three different types of landslides (rotational/translational slides, slope flows and local flows) using different pixel-size mapping units (5, 8, 10, 16 and 32 m) are compared and discussed. The obtained results show that the higher-resolution model (5 m) did not produce the best performance for any of the landslide typologies. The model with 8 m sized pixels displayed the optimal threshold size for slides and slope flows. In contrast, for local flows, an increasing trend of model prediction accuracy was reached with 32 m pixels, which was a higher value than that presented using 8 m pixels. The variable importance analysis demonstrated that the better performance of the 8 m cells was due to their effectiveness in capturing morphological conditions which favour slope instability (profile curvature and middle and high ridges).

One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data.

Landslide susceptibility assessment is an effective non-engineering landslide prevention at the regional scale. This study aims to improve the accuracy of landslide susceptibility assessment by using an optimized statistical index (SI) method. A landslide inventory containing 493 historical landslides was established, and 20 initial influencing factors were selected for modeling. First, a combination of GeoDetector and recursive feature elimination was used to eliminate the redundant factors. Then, an optimization method for weights of SI was adopted based on Gaussian process regression (GPR). Finally, the predictive abilities of the original SI model, the SI model with optimized factors (GD-SI), and the SI model with optimized factors and weights (GD-GPR-SI) were compared and evaluated by the area under the receiver operating characteristic curve (AUC) on the testing datasets. The GD-GPR-SI model has the highest AUC value (0.943), and the GD-SI model (0.936) also has a higher value than the SI model (0.931). The results highlight the necessity of factor screening and weight optimization. The factor screening method used in this study can effectively eliminate factors that negatively affect the SI model. Furthermore, by optimizing the SI weights through GPR, more reasonable weights can be obtained for model performance improvement.

The aim of the present study was to assess the suitability of mountainous areas for construction land on the basis of landslide susceptibility, to obtain the spatial distribution pattern of said suitability and to improve the existing theories and methods used to ascertain said suitability. Taking Hechuan District in Chongqing as the research area and using data relating to 754 historical landslide sites from 2000 to 2016, we selected 22 factors that influence landslides. The factors were classified into five types, namely topography and geomorphology, geological structure, meteorology and hydrology, environmental conditions and human activities. A landslide susceptibility model was constructed using the random forest algorithm, and safety factors of construction land suitability were established according to the results of landslide susceptibility, with the suitability of land for construction in mountainous areas assessed by combining the key factors (natural, social and ecological factors). The weights of the factors were determined through the use of expert approaches to classify the suitability of land for construction in the research area into five levels: prohibited, unsuitable, basically suitable, more suitable and most suitable. The results of the study show that: (1) The average accuracy of the tenfold cross-validation training set data of landslides reached 0.978; the accuracy of the test set reached 0.913; the accuracy of the confusion matrix reached 97.2%; and the area under curve (AUC) values of the training set, test set and all samples were 0.999, 0.756 and 0.989, respectively. Historical landslide events were found to be mostly concentrated in highly susceptible areas, and the landslide risk level in Hechuan District was mostly low or very low (accounting for 76.26% of the study area), although there was also a small proportion with either a high or very high risk level (9.25%). The high landslide susceptibility areas are primarily concentrated in the southern and southeastern ridge, in the valley and near water systems, with landslides occurring less frequently in the gentle hilly basin. (2) The suitability of land for construction in mountainous areas was strongly influenced by landslide susceptibility, distance from roads and distance from built-up areas; among such parameters, rainfall, elevation and lithology significantly influenced landslides in the region. (3) The land suitable for construction in the study area was highly distributed, mainly in urban areas where the three rivers meet and around small towns, with a spatial distribution pattern of high in the middle and low on both sides. Furthermore, the suitability of land for construction in Hechuan District was found to be primarily at the most suitable and more suitable levels (accounting for 84.66% of the study area), although a small proportion qualified for either the prohibited or unsuitable level (accounting for 15.72%). The present study can be extended and applied to similar mountainous areas. The landslide susceptibility map and construction land suitability map can support the spatial planning of mountainous towns, and the assessment results can assist with the development direction of mountainous towns, the layout of construction land and the siting of major infrastructure.

Landslide susceptibility maps (LSMs) have been used frequently by researchers for many years in prediction of the occurrence of landslides. Since many landslides have occurred there in the past, Izmir, which is the third largest city of Türkiye, was selected for landslide susceptibility assessment using geographical information systems (GIS) and remote sensing (RS) techniques. The aim of this study is to create a better landslide susceptibility map (LSM) for the Izmir metropolitan area and its surroundings by minimizing the shortcomings of some of the commonly used methods. For this purpose, four different LSMs were prepared using the logistic regression (LR), analytical hierarchy process (AHP), frequency ratio (FR) and index of entropy (IOE) methods with susceptibility classes ranging from extremely low to extremely high. These four maps were then overlaid. The highest susceptibility class was chosen for each pixel to form a combined landslide susceptibility map (CLSM). The final CLSM is a thematic map presenting landslide susceptibility using five different classes. The geo-environmental factors selected for use in this analysis were slope angle, slope aspect, lithology, slope curvature, elevation, density of discontinuity, stream power index (SPI), land use and distance from stream. Finally, the areas under receiver-operating characteristic (ROC) curves were employed to compare the predictive capability of the five models used. Overall, the Combined Method (CM) (AUC = 0.887) performed very well for landslide susceptibility assessment. Out of all the models, the IOE model (AUC = 0.841) had a slightly lower predictive capability than the CM model, and AHP (AUC = 0.816) was better than FR (AUC = 0.738) and LR (AUC = 0.727). It was observed that, compared to rural areas, residential areas of Izmir city are particularly susceptible to landslides.

Earthquakes cause a huge number of landslides and alter the regional landslide risk distribution. As a result, after a significant earthquake, the landslide susceptibility maps (LSMs) must be updated. The study goal was to create seismic landslide susceptibility maps containing landslide causative variables which are adaptable to great changes in susceptibility after the Jiuzhaigou earthquake (MS 7.0) and to perform a rapid update of the LSM after the earthquake by means of the distributed scatterer interferometric synthetic aperture radar (DS-InSAR) technique. We selected the territory of Jiuzhaigou County (southwestern China) as the study region. Jiuzhaigou is a world-renowned natural heritage and tourist area of great human and ecological value. For landslide susceptibility mapping, we examined the applicability of three models (logistic regression, support vector machine, and random forest) for landslide susceptibility mapping and offered a strategy for updating seismic landslide susceptibility maps using DS-InSAR. First, using logistic regression, support vector machine, and random forest techniques, susceptibility models of seismic landslides were built for Jiuzhaigou based on twelve contributing variables. Second, we obtained the best model parameters by means of a Bayesian network and network search, while using five-fold cross-validation to validate the optimized model. According to the receiver operating characteristic curve (ROC), the SVM model and RF model had excellent prediction capability and strong robustness over large areas compared with the LR models. Third, the surface deformation in Jiuzhaigou was calculated using DS-InSAR technology, and the deformation data were adopted to update the landslide susceptibility model using the correction matrix. The correction of deformation data resulted in a susceptibility class transition in 4.87 percent of the research region. According to practical examples, this method of correcting LSMs for the continuous monitoring of surface deformation (DS-InSAR) was effective. Finally, we analyze the reasons for the change in the revised LSM and point out the help of ecological restoration in reducing landslide susceptibility. The results show that the integration of InSAR continuous monitoring not only improved the performance of the LSM model but also adapted it to track the evolution of future landslide susceptibility, including seismic and human activities.

It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources is proposed. We focus on the changes in three aspects consisting of the vegetation condition, the surface deformation information and the landslide susceptibility. Firstly, the fractional vegetation cover of the landslide is extracted from optical remote sensing Gaofen-1 (GF-1) images using the dimidiate pixel model. Next, the surface deformation information of the landslide is derived from SAR remote sensing Sentinel-1A images applying the SBAS-InSAR method. Then, the landslide susceptibility based on GF-1, Sentinel-1A images and DEM data is computed using the analytic hierarchy process method. Finally, the spatio-temporal correlations of the vegetation condition, the surface deformation information and the landslide susceptibility are compared and interpreted. The Temi landslide is located along the Jinsha River and poses a high risk of blocking the river. Taking the Temi landslide as the study area, it is indicated from the results that the fractional vegetation cover, surface deformation information and landslide susceptibility reveal a consistency in the patterns of changes in spatial and temporal terms. As the surface deformation information improves, the status of the landslide vegetation also deteriorates and the landslide susceptibility becomes high, which indicates an increased probability of the creep and even the occurrence of landslides. In contrast, when the surface deformation information drops, the vegetation condition of the landslide becomes superior and the landslide becomes less susceptible, which means the likelihood of sliding declines. This study provides a new idea for a landslide monitoring method and potential way for natural disaster prevention and mitigation.

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.