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The sudden and unpredictable breach of landslide dams in the Yarlung Tsangpo river basin usually causes megafloods, posing great risks to human lives and infrastructures in the downstream areas. This study proposed an efficient and quantitative risk assessment framework of breach floods caused by landslide dam failures in the mainstream and tributaries of the Yarlung Tsangpo river basin with limited data. The impact of dam breach floods on human risks was evaluated. The flood attenuation along rivers, strategies for mitigating overlapping floods, and sensitivity analysis of human risks were also discussed. The results show that the developed framework successfully assessed flood risks caused by the breach of landslide dams. The flood attenuation ratio increased with river length but decreased with the peak discharge at dam site. A higher peak discharge and a larger inundated area downstream were predicted when the breach floods of two landslide dams, one in mainstream and the other in a tributary, overlapped at the confluence. The overlapping flood could be mitigated by reducing peak discharges of the two landslide dams or increasing time interval between the two peaks. The simulations also outlined the downstream peak discharge resulting from the cascading breach was larger than that of a single dam. However, it was smaller than the combined peak discharges of two separate dams, because the erosion during the breach of the downstream dam incurred energy dissipation. The human risks in the Pasighat village were greater when overlapping flood occurred due to the increased water depth and more hazardous inundated buildings. In the case of multi-peak floods, the warnings for the former peak flood would also warn the peak flood thereafter when individuals were notified multiple peaks. Otherwise, individuals might be misled by the warning of the previous peak flood, resulting in catastrophic flood impacts. A parametric analysis indicated that early evacuation warnings were needed to avoid serious loss of life and flood damages, especially in cases of dam breaches occurring at nighttime or for areas in close proximity to the dam site. [ABSTRACT FROM AUTHOR]

Landslides frequently produce dams that can cause widespread destruction when breached. Several factors influence the formation of landslide dams. This study used redundant analysis and hierarchical cluster analysis to investigate geomorphic features contributing to the formation of landslide dams. Eight landslide geomorphic features corresponding to the width of the river beneath the landslide and four geomorphic indices were selected to explore the geomorphic characteristics of 28 river-damming and 59 nondamming landslides in Taiwan. The redundancy analysis revealed that the geomorphic features derived for the river-damming landslides differed from those derived for the nondamming landslides. The width of the river beneath the landslide was noted to be strongly correlated with the aspect of nondamming landslides. Hierarchical cluster analysis was used to cluster the river-damming landslides into three clusters based on the geomorphic features influencing dam formation. Based on these clusters, the landslide site may be damming rivers under different geomorphic combination conditions. [ABSTRACT FROM AUTHOR]

Landslides resulting in complete river blockage have frequently occurred in the Three River Region (TRR) during the geomorphological evolution of the Tibetan Plateau. River-damming landslides occurring in low-relief regions of the TRR have received less attention compared to those in deeply-incised valleys. The 2.5 Mm3 Cuoduoqin rockslide originated from the south-facing hillslope of a southeast-east-trending ridge, leading to complete blockage of the Quzhaqu River. The original failure mainly involves blocky metamorphic limestone and phyllite. The Quzha Lake Fault providing rear rupture and two other groups of joints facilitating sidewise and toe releases are considered predisposing factors contributing to slope instability. Ongoing tectonic uplift and cyclic glaciations are considered preparatory factors, shifting the slope from stable to marginally unstable. A prehistoric earthquake, likely corresponding to an ancient rupture event on the active Nujiang Fault Zone (NJFZ), is deemed as the most probable trigger for this large rock slope failure. The 2D discrete element method (DEM) software UDEC is utilized to analyze the static slope stability and to reproduce the kinematic process of the rockslide. The static analysis indicates that the original rock slope was in equilibrium under natural conditions. The kinematic process can be divided into three phases: initial detachment within seconds after applying seismic load, downslope acceleration after crossing the slope knickpoint, and accumulation after traveling into the valley bottom. This case study, focusing on the development and kinematics of the Cuoduoqin rockslide, can help enhance the understanding of effective risk assessments of landslides in high-altitude and low-relief regions. [ABSTRACT FROM AUTHOR]

• Thoroughly compare XGBoost, RF, LR, and SVM models optimized by TPE algorithm. • Introduce SHAP algorithm to CLHA; global interpretation reveals factor importance. • Combine local unit interpretations and field surveys to reveal correlations. In the coseismic landslide hazard assessment (CLHA), advanced machine learning (ML) models have garnered significant attention due to their effectiveness in handling the complex relationships between landslides and various influencing factors. However, explaining the decision-making mechanisms of machine learning models that predict landslide spatial distribution based on these influencing factors remains challenging. This study compares the predictive performance of four models—XGBoost, RF, LR, and SVM—optimized using the TPE algorithm, and introduces the SHAP algorithm into the XGBoost model to achieve both global and local interpretations of CLHA. In various tests, the optimized XGBoost model demonstrated the best predictive performance, achieving an accuracy of 0.864 and an AUC value of 0.886. Global interpretations indicate that the occurrence of coseismic landslides is primarily influenced by triggering factors such as hypocentral distance and distance from the seismogenic fault. Terrain roughness and elevation, on the other hand, make significant contributions among the conditioning factors. Single-factor dependence plots indicate that the contribution of individual factors to landslides varies across different ranges of their feature values. Analysis of two-factor dependence plots reveals that interactions between factors are also crucial in influencing the occurrence of landslides. Combining field surveys with local interpretations confirms significant variations in the contributions of influencing factors within local ranges. The main innovation of this study lies in the integration of the SHAP algorithm into the CLHA model, revealing the decision-making mechanisms of the model for spatial prediction of coseismic landslides. [ABSTRACT FROM AUTHOR]

• Local and global XAI models explain produced landslide susceptibility maps. • Black-box models outperform white-box models by about 17 %. • Pairwise effects of factors have greater impacts on landslide susceptibility. • Local and global explanations identified slope as a major contributing factor. Artificial intelligence and machine learning have attracted significant attention in the preparation of landslide susceptibility maps (LSMs) over the years. Achieving considerable success, they frequently face criticism for their opaque nature and the limited capacity to explain and interpret the resulting LSMs. This study uncovers the inherent characteristics of conditioning factors by investigating both local and global driving forces influencing landslide events through the lens of explainable artificial intelligence. To accomplish this, black-box algorithms, including random forest, gradient boosting machines, and extreme gradient boosting, as well as white-box algorithms including logistic regression and decision trees, were employed to generate LSMs. Their internal structures were later illuminated with three global and one local explainable artificial intelligence (XAI) techniques. The results unveiled a significant superiority of black-box algorithms over white-box algorithms, demonstrating an improvement of up to 17 % in overall accuracy and 19 % in the area under the curve (AUC) score. Among them, the gradient boosting machines exhibited the highest performance, achieving an overall accuracy of 87.88 % and an AUC of 0.9382. Global explanation analyses revealed that landslide susceptibility was predominantly influenced by slope, elevation, distance to roads, and lithological units. On the other hand, local interpretations, conducted for three specific landslide cases, disclosed relative variations in the importance of causative factors such as slope and distance to rivers on landslide occurrences. Overall, this study illustrates the potential utility of XAI tools in enhancing the transparency of generated maps and elucidating the underlying causes of specific landslide occurrences. [ABSTRACT FROM AUTHOR]

• Semi-automatic technique has been utilized using VHR imagery to develop landslides inventory. • Most prone transportation infrastructure has been identified using susceptibility analysis. • Temporal recovery of landslides based on the vegetation change has been evaluated. • Rainfall induced landslides has been identified using timeseries data. In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide's temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area. [ABSTRACT FROM AUTHOR]

Landslide hazards affect the security of human life and property. Landslide hazard maps are essential for landslide prevention and mitigation. In this study, the success of machine learning and statistical methods in predicting landslide hazards in and around the district center of Maden, Elazığ province, within the borders of Turkey, was analyzed, and their performances were compared. The Random Forest method correctly predicted 1.398 of the 1.425 landslide points in the training dataset, but was incorrect on 27 points. The same method predicted 1942 of the 2075 landslide-free points in the training dataset, but incorrectly predicted 133 points as landslide-exposed. As a result of the study, it is evident that the Random Forest and M5P Rule Tree methods yield more successful results than the Frequency Ratio method. In the study area, the landslide hazard is concentrated in areas close to the East Anatolian Fault and in areas with steep slopes. Lithology, slope, and seismicity have been identified as important triggering factors for landslides in the region. It is expected that machine learning methods, which operate with high levels of accuracy, will make a significant contribution to the prediction of landslide hazards. [ABSTRACT FROM AUTHOR]

In order to reveal the destructive mechanism of loess landslide induced by stagnant water on the combined surface, and to clarify the influence of the main control factors, this paper takes a typical loess landslide in northern Shaanxi as the research object, analyzes the structure of the rock and soil body, and the excavation and filling construction through the geohazard survey, and analyzes the process of traction sliding caused by the stagnant water on the combined surface at the different stages of the project by combining with the calculation of the stability of the slope body. Further the article analyses the process of traction sliding caused by water on the combined area due to construction by means of a discrete element model, and delves into the mechanism of strength reduction of saturated loess. The results show that: 1) the combined surface stagnant water type loess landslide has the characteristics of sudden sliding and rapid evolution, which is highly hazardous and difficult to prevent and control; 2) the slope destabilization is controlled by the engineering geological conditions, and the slope excavation changes the original mechanical equilibrium conditions of the slope, which provides the dynamic conditions for the traction sliding of the slope; 3) the change of the hydrogeological environment results in the obstruction of the natural drainage channel, which leads to the formation of continuous sliding surface due to stagnant water on the combined surface, and the formation of a continuous sliding surface due to stagnant water on the combined surface. Surface stagnant water to form a continuous slippery surface, inducing the overall destabilization of the slope damage; 4) loess strength index with the increase of saturation and the exponential function form of reduction, and when the saturation degree reaches more than 80%, the strength index of the soil body to reach the basic stability. The article expanding the ideas of landslide control and analysis, and the research results will provide a theoretical basis for the design of junction landslide management in the loess areas of northern Shaanxi. [ABSTRACT FROM AUTHOR]

Landslide inventories are crucial for evaluating susceptibility, hazards, and risks, and for devising resilience strategies in mountainous regions. This importance is amplified in the context of climate change, as existing inventories might not adequately reflect changing stability conditions. In May 2023, the Emilia-Romagna region of Italy was hit by two major rainfall events, leading to widespread flooding and the triggering of thousands of landslides. Predominantly, these were shallow debris slides and debris flows, occurring on slopes previously deemed stable based on historical data with no prior landslides recorded. Our team supported the Civil Protection Agency through field surveys and mapping efforts to pinpoint and record these landslides, prioritizing areas critical to immediate public safety and focusing on thorough mapping for future recovery planning. The outcome is a detailed map of all landslides induced by these events, manually identified using high-resolution aerial photography (0.2 m pixel resolution, RGB+NIR four bands) and categorized with the help of a 3D viewer. This comprehensive landslide inventory, comprising 80997 polygons, has been made openly accessible to the scientific community. [ABSTRACT FROM AUTHOR]

Chefchaouen province, located in northern Morocco, is prone to landslides because of its geological features, climate change, and human activities. We investigated landslide dynamics in this mountainous province using a multi-criteria spatial approach and GIS, applying the Analytical Hierarchy Process (AHP). Our findings identified 1267.877 km2 as low-risk, 1695.334 km2 as moderate-risk, and 904.2071 km2 as high-risk, mainly in the northern and northwestern regions. Forests were the most susceptible at 13.83%, followed by agricultural and bare lands, with water bodies least affected. Contributing factors include deforestation, wildfires, and agricultural practices. Our results highlight the need for targeted risk mitigation and continuous land use monitoring. Strategies like slope stabilisation and reforestation can improve landslide prediction and prevention, enhancing community resilience in Chefchaouen. This study’s innovative approach and insights into landslide prediction and prevention provide valuable knowledge to the field. [ABSTRACT FROM AUTHOR]

ABSTRACT Landslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate. [ABSTRACT FROM AUTHOR]

The selection and quality of the evaluation unit directly affect the rationality of the evaluation results of regional landslides, in which the slope unit as the evaluation unit can better describe the landform characteristics and topographic changes, and can better reflect the natural environmental conditions of landslides. However, there is currently no optimization method for slope unit partitioning, which restricts the application of slope units in regional landslide disaster evaluation. Therefore, this paper proposes a R-MSS (Refined Multi-Scale segmentation) method based on object-based image analysis (OBIA). Typical mountainous areas in southwest China and hilly areas in southeast China were selected and compared using hydrological analysis, curvature watershed, and MSS methods. The results show that the R-MSS method can effectively extract large-area slope units without a lot of manual modification. The divided slope units have uniform size, small spatial difference and good morphological effect. The overall effect is significantly better than other methods, especially in the high and steep mountainous areas with complex and changeable terrain, its segmentation performance is especially outstanding. The research results can serve for regional landslide hazard evaluation and improve the reliability of regional landslide hazard analysis results to a certain extent. [ABSTRACT FROM AUTHOR]

Landslides are common geohazards worldwide, resulting in significant losses to economies and human lives. Data-driven approaches, especially machine learning (ML) models, have been widely used recently for landslide susceptibility mapping (LSM) by extracting features from geospatial variables based on their contribution to landslide occurrences using known distributions of landslides as the training dataset. However, challenges remain in applying ML models for LSM models due to the scarcity and uneven spatial distribution of landslide data coupled with the spatial heterogeneity of hillslope conditions. Moreover, ML models developed with limited data often exhibit unexpected behaviors, resulting in poor interpretability and predictions that deviate from intuitive expectations and established domain knowledge. To overcome these challenges, this study proposes a physics-guided machine learning (PGML) framework that integrates landslide domain knowledge into ML models for LSM. The PGML framework was developed and assessed using a detailed debris flow inventory from a storm event in the Colorado Front Range. Based on the infinite slope model, the factor of safety for the study area was first determined and was subsequently used to constrain the prediction of ML models through a modified loss function and measure the physics consistency of model predictions. To evaluate the robustness and generalizability of the models, this study uses geographical sample selections for model performance evaluation, where ML models are trained and tested across heterogeneous ecoregions. The results of this study demonstrated the efficacy of both physics-based and data-driven methods in determining landslide susceptibility in the study area; however, pure data-driven ML models produced physically unrealistic results and poor generalization performance in new ecoregions. With the incorporation of physical constraints, the PGML model demonstrated notable enhancements in physics consistency and generalization capability, along with reduced model uncertainties across various ecoregions, surpassing the performance of benchmark ML models. [ABSTRACT FROM AUTHOR]

The transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake‐induced landslides, landslide‐dammed lakes and landslide‐induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high‐resolution satellite imagery and open‐source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality‐controlled inventory represents a yearly record of landslides from 2011 to 2020. A data‐driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation‐based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development. [ABSTRACT FROM AUTHOR]

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment. [ABSTRACT FROM AUTHOR]

Landslides pose significant threats to life and property, particularly in mountainous regions. To address this, this study develops a landslide susceptibility model integrating Earth Observation (EO) data, historical data, and Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) ground movement results. The model categorizes areas into four susceptibility classes (from Class 1 to Class 4) using a multi-class classification approach. Results indicate that the Xtreme Gradient Boosting (XGB) model effectively predicts landslide susceptibility with area under the curve (AUC) values ranging from 0.93 to 0.97, with high accuracy of 0.89 and a balanced performance across different susceptibility classes. The integration of MT-InSAR data enhances the model's ability to capture dynamic ground movement and improves landslide mapping. The landslide susceptibility map generated by the XGB model indicates high susceptibility along the Pacific coast. The optimal model was validated against 272 historical landslide occurrences, with predictions distributed as follows: 68 occurrences (25%) in Class 1, 142 occurrences (52%) in Class 2, 58 occurrences (21.5%) in Class 3, and 4 occurrences (1.5%) in Class 4. This study highlights the importance of considering temporal changes in environmental conditions such as precipitation, distance to streams, and changes in vegetation for accurate landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

The article discusses the landslides triggered by the 2024 Noto Peninsula earthquake in Ishikawa Prefecture, Japan. The earthquake, with a magnitude of 7.6, caused extensive damage and claimed 244 lives. Over 2300 landslides were triggered by the earthquake, including the Machinomachi and Ichinose landslides, which destroyed multiple houses. The article presents the results of field surveys, ring shear tests, and computer simulations to understand the mechanism of the landslides and validate the simulation results. The study highlights the importance of understanding long-runout landslides for future earthquake risk assessment. [Extracted from the article]

The formation and failure of landslide dams is an important and understudied, multi-hazard topic. A framework of landslide dam formation susceptibility evaluation was designed for large-scale studies to avoid the traditional dependence on landslide volume calculations based on empirical relationships, which requires comprehensive local inventories of landslides and landslide dams. The framework combines logistic regression landslide susceptibility models and global fluvial datasets and was tested in Italy and Japan based on landslide and landslide dam inventories collected globally. The final landslide dam formation susceptibility index identifies which river reach is most prone to landslide dam formation, based on the river width and the landslide susceptibility in the adjacent delineated slope drainage areas. The logistic regression models showed good performances with area under the receiver operating characteristics curve values of 0.89 in Italy and 0.74 in Japan. The index effectively identifies the probability of landslide dam formation for specific river reaches, as demonstrated by the higher index values for river reaches with past landslide dam records. The framework is designed to be applied globally or for other large-scale study regions, especially for less studied data-scarce regions. It also provides a preliminary evaluation result for smaller catchments and has the potential to be applied at a more detailed scale with local datasets. [ABSTRACT FROM AUTHOR]

The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables' importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems. [ABSTRACT FROM AUTHOR]

Landslides pose significant impact on human life and society such as loss of livelihood, destruction of infrastructure, and damage to natural resources around the world. Due to existing complications in conditional factors of landslide, mapping and predicting landslide occurrences with high accuracy needs more attention. In light of this, we aim to develop an ensemble landslide susceptibility model named as support vector regression–grasshopper optimization algorithm (SVR–GOA). This model is validated along with other landslide susceptibility models such as artificial neural network (ANN), boosted regression tree (BRT), and elastic net models. The present study carried out over the Kalaleh Basin in Iran, in which we selected 140 landslides with 16 conditional factors to construct a geographic database of the region. The multicollinearity analysis was done on the hazard conditioning factors using variance inflation factor and tolerance indices. Similarly, significance of these factors and their association with selected locations were identified through random forest method. The state of the art of the study is implementing SVR-GOA in landslide susceptibility mapping, including this model we use other landslide models such as ANN, BRT, and elastic net for validation and development using the area under the curve (AUC), kappa, and root mean squared error values. Our results show lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were found to be the most influential conditioning factors. We have also observed that, despite accurate prediction, SVR-GOA outperforms the others by showing the highest AUC values around AUC = 0.930 and others show ANN (AUC = 0.833), BRT (AUC = 0.822) and elastic net (AUC = 0.726) respectively. This innovative approach to landslide mapping using SVR-GOA ensembles would enhance the advancement of landslide research at multiple scales. [ABSTRACT FROM AUTHOR]

The purpose of this research is to apply and compare the performance of the three machine learning algorithms-Naive Bayes (NB), kernel logistic regression (KLR), and alternation decision tree (ADT) to come up with landslide susceptibility maps for Pengyang County, a landslide-prone area in Ningxia Hui Autonomous Region, China. In the first phase, we constructed a landslide inventory map consisting of 972 landslides and the same quantity of non-landslides based on digital elevation model analysis, survey data and satellite images, then combined the two databases and classified into training and validating subsets randomly at the ratio of 70:30. Secondly, 13 conditional factors were prepared, and feature selection was performed using average merit. Subsequently, we used the area under the receiver operating characteristic curve (AUC), root mean square error, mean squared error, and frequency ratio precision to test the validity and prediction ability of the models. This outcome demonstrated that three models are all predictive and can generate adequate results in the study scope, and the ADT model is entitled with the best performance, whose AUC values are 0.844 for the training dataset and 0.838 for the validation dataset. The next is KLR (0.811 for the training dataset, 0.814 for the validation dataset) and then NB (0.808 for the training dataset, 0.797 for the validation dataset) models. Meanwhile, the frequency ratio precision of ADT model is 0.971, which is higher than KLR (0.844) and NB (0.810). The suggested landslide susceptibility map and corresponding method enable researchers and local authorities in future decision-making for geological disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

Natural hazards, such as flood, landslide, and erosion, are the reality of human life. spatial prediction of these hazards and their effectiveness factors are extremely important. The main goal of this study was to prepare multi-hazard probability mapping (flood, landslide, and gully erosion) of the Gorganrood Watershed. In addition, different machine learning models such as Random Forest (RF), Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Multivariate Adaptive Regression Spilines (MARS) were applied. First, a flood, landslide, and gully erosion inventory map was produced using GPS in the field surveys and Google Earth. Factors affecting the hazards were identified, and GIS maps were prepared. The MARS model (AUC = 99.1%) provided the highest predictive performance for flood, landslide, and gully erosion hazards. However, for flood and landslide, the RF model exposed excellent and good performance, respectively. According to the variable importance analysis, drainage density (89.4%), digital elevation model (30.5%), and rainfall (41.7%) were consistently highly ranked variables for flood, landslide, and gully erosion, respectively. Multi-hazard maps can be a valuable tool for the conservation of natural resources and the environment, as well as for sustainable land use planning in multi-hazard-prone areas. [ABSTRACT FROM AUTHOR]

The Garhwal and Kumaun regions of the Himalayas of India have experienced rapid urbanisation due to the expansion of the national highway (NH-58) in Uttarakhand, which has a significant impact on the frequency and intensity of landslides. Therefore, this study assesses the impact of road expansion on landslide susceptibility in the Himalayas by examining landscape changes within 1 km, 2 km, and 3 km buffer zones around a major highway. Land use and land cover (LULC) from the years 2000 and 2023 were classified using Random Forest (RF) modelling to assess landslide susceptibility due to landscape change. Twelve key parameters were selected for susceptibility modeling and colinearity was tested by multicollinearity analysis to ensure robustness. The RF models were optimised using particle swarm optimisation (PSO) to model landslide susceptibility with higher precision, and their effectiveness was confirmed by receiver operating characteristic (ROC) and precision-recall curves. In addition, a polynomial regression analysis was used to investigate the complex relationships between landscape changes and landslide susceptibility. The susceptibility models showed high accuracy with area under the curve (AUC) values of 0.9083 and 0.9068. The results showed significant landscape changes affecting landslide susceptibility: The forest area decreased by 15.74 km² from 2000 to 2023, while the built-up area increased by 15.41 km². In particular, the zone with very low susceptibility to landslides within the 1 km buffer decreased by 5.569 km² and the zone with high susceptibility within the 3 km buffer increased by 4.972 km². The polynomial regression analysis showed that the built-up areas near the motorway decreased the most by 1.260 km², while the forest areas further away from the corridor increased by up to 4.585 km². This analysis illustrates the significant influence of road proximity on landscape dynamics and landslide risk and provides important insights for policy and land use planning in the rapidly developing Himalayan regions. [ABSTRACT FROM AUTHOR]

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million. [ABSTRACT FROM AUTHOR]

The extreme meteorological events caused by climate change have increasingly caused serious clustered landslides. A systematic and timely inventory of triggered landslides is a prerequisite for quantifying and evaluating the impact of extreme meteorological events. In addition, a landslide inventory can provide basic data for any subsequent analysis of the event or be used innovatively in landslide risk analysis. The Rainfall-induced Landslides in Beijing (RLBJ) inventory presented here contains data on 15,383 rainfall-induced shallow landslides triggered by a single extreme precipitation event in July 2023 in an area of ~3,250 km2 in western mountainous areas of Beijing, China. High-resolution satellite images before and after this rainstorm event were used to visually analyze the landslides. All landslides were reported as vectorized points and polygon features and classified according to their motion forms. This inventory is now freely available for the benefit of international geohazard researchers. [ABSTRACT FROM AUTHOR]

This study addresses impact of increasing frequency of natural hazards, particularly in mountainous regions like the Himalayas. The research develops a comprehensive multi-hazard susceptibility mapping methodology. . The integrated methodology involving majorly machine learning, remote sensing and GIS tools is applied to generate the multi-hazard susceptibility map of the test site. This integrated approach offers a nuanced understanding of hazard interactions, providing a robust framework for disaster risk reduction, government and non-government agencies in planning future developmental projects and infrastructures in the study area. The study’s findings show that 11.30% of the area is labeled as highly susceptible to multi-hazards. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive study on the stability and failure mechanism of landslides in northwest mountainous areas under rainfall and earthquake conditions. The study focuses on the Lijie Beishan landslide as a typical case and combines on-site geological surveys with numerical simulations to evaluate its stability. The results show that the stability of the landslide decreases under static, rainfall, and earthquake conditions, with different failure modes observed. The study also compares different stability evaluation methods and provides valuable references for landslide stability evaluation in the region. The given text discusses the stability and failure process of a landslide using numerical simulation methods. The lower layer of the landslide is highly fragmented and prone to sliding due to its poor engineering properties. Human activities such as slope cutting and inappropriate land use contribute to the instability and sliding of the landslide. The text describes the principles and parameters used in the numerical simulation, including the use of finite element software and a viscoelastic constitutive model. The results of the simulation show the potential sliding surface and displacement of the landslide under different conditions. This document presents the results of a stability analysis of landslides in Lijie Beishan. The analysis was conducted under different conditions, including normal static, rainfall, and seismic conditions. The results indicate that the landslides have different failure modes and maximum deformations depending on the condition. The stability factor values were calculated to determine the stability state of the landslides, with values below 1 indicating instability. The analysis provides valuable information for understanding the [Extracted from the article]

Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given area. However, due to the rarity of multi-temporal landslide inventory, conventional data-driven LSMs are primarily generated by spatial causative factors, while the temporal factors remain limited. In this study, a spatiotemporal LSM is carried out using machine learning (ML) techniques to assess rainfall-induced landslide susceptibility. To achieve this, two landslide inventories are collected for southwestern Pennsylvania: a spatial inventory and a multi-temporal inventory, with 4543 and 223 historical landslide samples, respectively. The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution. A novel paradigm of data augmentation through non-landslide sampling based on domain knowledge is applied to leverage both spatial and temporal information for ML modeling. The results show that the spatiotemporal ML model using the proposed data augmentation predicts well rainfall-induced landslides in space and time across the study area, with a value of 0.86 of the area under the receiver operating characteristic curve (AUC), which makes it an effective tool in rainfall-induced landslide hazard mitigation and forecasting. [ABSTRACT FROM AUTHOR]

Recent advancements have seen a pervasive application of machine learning methodologies in assessing the susceptibility of geological hazards. A pivotal element influencing the accuracy of model predictions resides in the prudent selection of model parameters within machine learning frameworks. The objective of this study is to develop a robust landslide susceptibility assessment model by refining the support vector machine (SVM) model through the employment of the Bayesian algorithm for hyperparameter optimization. The southern part of the Qinghai-Tibet Plateau, focusing on major highways, is selected as the study area. Nine influencing factors, namely the elevation, slope, aspect, profile curvature, lithology, topographic wetness index, normalized difference vegetation index, distance to faults, and distance to rivers, are selected as the conditioning variables instrumental in evaluating the likelihood of collapse occurrences. Secondly, data from field surveys involving 351 landslides and randomly generated non-landslide data are utilized in a balanced 1:1 ratio to construct the training and testing datasets. Next, the cross-validation loss rate of the SVM model is selected as the objective function, and the Bayesian algorithm is used to optimize the BoxConstraint and KernelScale parameters of the SVM model, resulting in a Bayesian optimization-based SVM model. The results show that, within a five-fold cross-validation framework, the model yields 99.15% and 96.32% accuracy for the training and testing datasets, respectively. Concurrently, the area under the receiver operating characteristic curve values are recorded at 99.76% and 98.67% for the respective datasets, highlighting a notable level of predictive proficiency. Furthermore, factor importance ranking reveals lithology and elevation as the most influential, with partial dependence plots identifying high susceptibility areas between elevations of 2916 and 3954 m under soft lithology conditions. A collapse susceptibility map encompassing the entire study area is encompassing, categorizing the study area into extremely high (7.79%), high (13.38%), moderate (29.99%), and low (48.84%) susceptibility zones. [ABSTRACT FROM AUTHOR]

Coastal landslides pose significant threats to critical infrastructure in energy-dependent regions like the Assaluyeh anticline. To safeguard communities and economic stability, it is crucial to address uncertainties in coastal landslide susceptibility assessments. This necessity extends beyond scientific exploration and requires pioneering fuzzy logic methodologies, to enhance predictive accuracy. The Assaluyeh anticline is in the Persian Gulf's Zagros intercontinental region is of paramount importance, influencing Iran's energy infrastructure. To comprehend coastal landslide risk, our innovative study focuses on the Assaluyeh anticline's coastal areas. We combine 13 conditioning factors, blending traditional geological characteristics with advanced technology, including lithology, land-cover, topographic wetness index (TWI), elevation, and slope angle, using AI-driven modeling to create predictive landslide susceptibility maps. This approach surpasses conventional techniques, offering improved early warning systems and disaster management. Our research's groundbreaking aspect is its forward-looking approach, utilizing fuzzy logic to adapt to real-world conditions by embracing imprecision and uncertainty in data. We integrate historical development data, local knowledge, and forward-thinking fuzzy logic techniques to promote community-driven risk management and resilience in coastal environments. This study envisions the future, revolutionizing coastal landslide assessment and management, inspiring interdisciplinary collaboration, community engagement, and fuzzy logic-driven innovation in coastal disaster risk reduction on a global scale. [ABSTRACT FROM AUTHOR]

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region's climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale. [ABSTRACT FROM AUTHOR]

Machine learning models have been increasingly popular in landslide susceptibility mapping based on the correlations among landslides and their inducing factors. However, mislabeled data in model training sets would deteriorate model accuracy. This study employed a Bayesian network to analyze influencing factors on landslides in Fujian Province, China, prone to typhoons and landslides. An inventory of 5,992 historical landslides informs Bayesian network modeling, with ten geoenvironmental factors as predictors. We introduced a progressive noise filtering method to mitigate the mislabeling effects of non-landslide points. The results show that altitude, wind speed, and lithology are the most important factors of landslides in the study area. The accuracy of the resultant landslide susceptibility map was verified using the area under the receiver operating characteristic curve (AUC) and Moran's I index. The AUC value was improved from 0.838 to 0.931 during the progressive noise filtering. The correlation between historical landslide number density (LND) and resultant landslide susceptibility index (LSI) was evaluated. The Local Indicators of Spatial Association based on Moran's I index shows consistent distribution patterns for high LND and high LSI regions. This study provides a useful reference for reliable landslide susceptibility mapping in the study area and similar areas. [ABSTRACT FROM AUTHOR]

Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model's prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment. [ABSTRACT FROM AUTHOR]

Due to the acceleration of construction of civil, industrial and infrastructure facilities in our republic and the expansion of their area, the construction of many facilities is carried out in mountainous, and foothill areas with complex relief and geomorphological conditions, which in most cases are potentially dangerous landslide slopes. To protect these facilities from the negative impact of dangerous geological, naturaltechnogenic, and anthropogenic processes, to assess the risks arising from the impact of these processes on building structures, to promptly prevent negative phenomena and dangerous situations, to assess the engineering and geological conditions of the construction area to determine the stability of the slope, conducting research in these areas is of great practical importance. Mountainous regions are characterized by the complexity and diversity of geological, hydrological, hydrogeological, and tectonic conditions. Landslides are the most common natural and man-made processes that pose a threat to the safe operation of infrastructure facilities, civil and industrial construction in mountainous and foothill areas. In addition to infrastructure facilities, landslides can destroy vegetation, destroy the habitat of fauna, and also destroy fertile soils located in the area of their activity (Nikolic, 2015; Krivoguz & Bespalova, 2020). The main objective of this article is to apply the results of practically implemented and verified studies in other territories of our republic with similar geomorphological structure and engineering-geological conditions, to correctly assess the negative manifestations caused by landslides and other man-made and anthropogenic impacts, as well as to calculate the stability of the slope and scientifically and technically substantiate measures for engineering protection against landslides. [ABSTRACT FROM AUTHOR]

Tunnel engineering is a complex and multidisciplinary field that requires the integration of geological expertise, advanced modeling techniques, and practical engineering solutions. The research compiled in the Special Issue "Tunnels and Tunneling" makes significant contributions to the field by addressing the diverse geological conditions and intricate challenges inherent in tunnel construction. These insights are crucial for enhancing the safety, efficiency, and sustainability of tunnel projects worldwide. The studies in this Special Issue provide a comprehensive understanding of the various challenges and innovative solutions in tunnel engineering. They offer valuable insights and practical guidelines for designing, constructing, and maintaining safe and stable tunnel structures across different geological settings. In addition, geological challenges in specific regions, such as the Three Gorges Reservoir area, the Hengduan Mountains, and the Tibetan Plateau, require tailored approaches. A key theme in many of the comparative studies is the importance of accurate risk assessment to ensure tunnel safety. In regions prone to geological hazards, landslide susceptibility mapping and risk assessment are critical. Innovative approaches, such as machine learning models, are highlighted for their potential to predict and manage landslide risks effectively. [ABSTRACT FROM AUTHOR]

Quantitative calculation of single landslide risk has great significance for the prevention and treatment of landslides, through analysing the slope stability under different rainfall recurrence periods. In this study, the rainfall of the past 40 years in Xun'wu County of China is counted and the rainfall during the return periods of 10, 20 and 50 years are calculated to form three different rainfall conditions. Then, the stability of Cheng'nan landslide in Xun'wu County is calculated by the Geo‐Studio 2007 software, and the probability of landslide occurrence is obtained by Monte Carlo theory under these three conditions. Next, the field investigation is employed to obtain the statistical results of the buildings and personnel in the affected area of Cheng'nan landslide. Finally, the risk of economic loss and casualty under the three conditions are calculated. It was demonstrated that: (1) Under the three conditions, the safety factor decreased gradually, the rate of decrease was slower in the first 3 days and faster in the middle period and there was still a downward trend after the end of the rain. (2) The probability of landslide occurrence during the rainfall return periods of 10, 20 and 50 years were 1.77%, 2.97% and 1.61%, respectively. Besides, the risk index of landslide was the highest under the condition of 20‐years rainfall return period. (3) The economic loss risk and casualty risk in the rainfall return periods of 10, 20 and 50 years were 122,700‐yuan and 4.11 people, 205,900‐yuan and 6.89 people, as well as 11,600‐yuan and 3.74 people, respectively. [ABSTRACT FROM AUTHOR]

In the process of landslide susceptibility prediction (LSP) modelling, there are some problems in the model dataset relating to landslide and non‐landslide samples, such as landslide sample errors, subjective randomness and low accuracy of non‐landslide sample selection. In order to solve the above problems, a semi‐supervised machine learning model for LSP is innovatively proposed. Firstly, Yanchang County of Shanxi Province, China, is taken as the study area. Secondly, the frequency ratio values of 12 environmental factors (elevation, slope, aspect, etc.) and the randomly selected twice non‐landslides are used to form the initial model datasets. Thirdly, an extreme gradient boosting (XGBoost) model is adopted for training and testing the initial datasets, so as to produce initial landslide susceptibility maps (LSMs) which are divided into very low, low, moderate, high and very high susceptibility levels. Next, the landslide samples in initial LSMs with very low and low susceptibility levels are excluded to improve the accuracy of landslide samples, and the unlabelled twice non‐landslide samples in initial LSMs with low and very low susceptibility levels are randomly selected to ensure the accuracy of non‐landslide samples. These new obtained landslide and non‐landslide samples are reimported into XGBoost model to construct the semi‐supervised XGBoost (SSXGBoost) model. Finally, accuracy, kappa coefficient and statistical indexes of susceptibility indexes are adopted to assess the LSP performance of XGBoost and SSXGBoost models. Results show that SSXGBoost model has remarkably better LSP performance than that of XGBoost model. Conclusively, the proposed SSXGBoost model effectively overcomes the problems that the accuracy of landslide samples needs to be further improved and that non‐landslide samples are difficult to select accurately. [ABSTRACT FROM AUTHOR]

The purpose of this study is to utilize three machine learning models—random forest, logistic regression and extreme gradient boosting—to assess the landslide susceptibility of Wushan County and compare the predictive performance of each algorithm. To achieve this, the database was randomly divided into training (80%) and testing (20%) datasets. We considered the impact of soil thickness, which has rarely been explored in previous research. A spatial database of 19 conditioning factors related to the occurrence of a landslide was constructed to develop the landslide susceptibility maps. Thereafter, three models were estimated and compared using metrics such as accuracy, recall, F1 score and area under the receiver operating characteristic curve (AUC). The results show that the random forest model with the testing dataset has higher accuracy (0.848), F1 score (0.740) and AUC (0.904) values. Soil thickness is found to play a significant role in the occurrence of a landslide. The quantitative analysis of landslide susceptibility maps indicates the superiority of the random forest model. Finally, the outcomes of the random forest model incorporating multicollinearity analysis and factor selection were thoroughly discussed. In conclusion, the random forest is the recommended algorithm for evaluating landslide susceptibility to aid in disaster management in Wushan County. [ABSTRACT FROM AUTHOR]

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets. [ABSTRACT FROM AUTHOR]

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management. [ABSTRACT FROM AUTHOR]

The assessment of landslide susceptibility often overlooks the influence of forests on shallow landslide mobility, despite its significance. This study delved into the impact of forest presence on shallow landslide mobility during intense rainfall in Mengdong, China. Field investigations were coupled with the analysis of pre- and post-rainfall remote sensing (RS) images to delineate landslides. The ratio of landslide height (H) to travel distance (L) from a digital elevation model (DEM) were used to calculate landslides mobility. Preceding the event, forest coverage was evaluated using the normalized difference vegetation index (NDVI) derived from multiband RS image. The research identified 1531 shallow landslides in the area, revealing a higher concentration of landslides on slopes with elevated NDVI. Results indicated that disparities in soil permeability and cohesion, generating pore water pressure (PWP), triggered clusters of shallow landslides. Shallow landslides exhibit a higher propensity on slopes with elevated NDVI. The dimensions (height and area) of these identified shallow landslides typically exhibit a positive correlation with NDVI, consequently resulting in longer travel distances for landslides occurring on higher NDVI slopes. The average H/L ratio of all identified landslides was about 0.63. H/L generally increases with NDVI and decreases with landslide area. However, due to river channel restrictions, the H/L increases with slope gradient. The findings suggest that the high permeability of areas with tree roots poses a risk to the shallow stability of slopes, yet trees contribute to mitigating landslide mobility. [ABSTRACT FROM AUTHOR]

This study examined the applicability of factorization machines with quantum annealing (FMQA) to the field of landslide risk assessment for two specific black‐box optimization problems, hyperparameter optimization (HPO) for metamodeling and metamodel‐based simulation optimization (MBSO) targeting granular flow simulation using discrete element method (DEM). These two optimization problems are solved successively: HPO is first performed to determine the hyperparameters of the Gaussian process regression (GPR) metamodel, which is then used as a low‐cost, fast approximate solver of granular flow simulations for MBSO. After conducting a series of granular flow simulations using DEM, a metamodel is created that outputs a risk index of interest, the run‐out distance, from its input parameters by employing GPR with two hyperparameters, length‐scale and signal variance. Subsequently, HPO is performed to obtain the optimal set of hyperparameters by applying FMQA and other optimization methods using another set of hyperparameters determined using the gradient‐ascent method as the reference solution. Finally, using the metamodel created by each optimization method as an approximate solver for DEM simulations, MBSO is performed to find the optimal target output, the maximum run‐out distance, in the space of physical input parameters for risk assessment. A comparison of the performance of FMQA with that of other methods shows that FMQA is competitive in terms of efficiency and stability with state‐of‐the‐art algorithms such as Bayesian optimization. [ABSTRACT FROM AUTHOR]

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The rapidly expanding Himalayan road network connects rural mountainous regions. However, the fragility of the landscape and poor road construction practices lead to frequent mass movements alongside roads. In this study, we investigate fully or partially road-blocking landslides along National Highway (NH-7) in Uttarakhand, India, between Rishikesh and Joshimath. Based on an inventory of >300 landslides along the ∼250 km long corridor following exceptionally high rainfall during September and October 2022, we identify the main controls on the spatial occurrence of mass-movement events. Our analysis and modeling approach conceptualizes landslides as a network-attached spatial point pattern. We evaluate different gridded rainfall products and infer the controls on landslide occurrence using Bayesian analysis of an inhomogeneous Poisson process model. Our results reveal that slope, rainfall amounts, lithology and road widening are the main controls on landslide occurrence. The individual effects of aggregated lithozones are consistent with previous assessments of landslide susceptibilities of rock types in the Himalayas. Our model spatially predicts landslide occurrences and can be adapted to other rainfall scenarios, thus having potential applications for efficiently allocating efforts for road maintenance. To this end, our results highlight the vulnerability of the Himalayan road network to landslides. Climate change and increasing exposure along this pilgrimage route will likely exacerbate landslide risk along the NH-7 in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Extreme rainfall events in Andean basins frequently trigger landslides, obstructing river channels and causing flash flows, loss of lives, and economic damage. This study focused on improving the modeling of these events to enhance risk management, specifically in the La Liboriana basin in Salgar (Colombia). A cascading modeling methodology was developed, integrating the spatially distributed rainfall intensities, hazard zoning with the SLIDE model, propagation modeling with RAMMS using calibrated soil rheological parameters, the distributed hydrological model TETIS, and flood mapping with IBER. Return periods of 2.33, 5, 10, 25, 50, and 100 years were defined and applied throughout the methodology. A specific extreme event (18 May 2015) was modeled for calibration and comparison. The spatial rainfall intensities indicated maximum concentrations in the northwestern upper basin and southeastern lower basin. Six landslide hazard maps were generated, predicting landslide-prone areas with a slightly above random prediction rate for the 2015 event. The RAMMS debris flow modeling involved 30 simulations, indicating significant deposition within the river channel and modifying the terrain. Hydraulic modeling with the IBER model revealed water heights ranging from 0.23 to 7 m and velocities from 0.34 m/s to 6.98 m/s, with urban areas showing higher values, indicating increased erosion and infrastructure damage potential. [ABSTRACT FROM AUTHOR]

Several landslide susceptibility (LS) maps at various scales of analysis have been performed with specific zoning purposes and techniques. Supervised machine learning algorithms (ML) have become one of the most diffused techniques for landslide prediction, whose reliability is firmly based on the quality of input data. Site-specific landslide inventories are often more accurate and complete than national or worldwide databases. For these reasons, detailed landslide inventory and predisposing variables must be collected to derive reliable LS products. However, high-quality data are often rare, and risk managers must consider lower-resolution available products with no more than informative purposes. In this work, we compared different ML models to select the most accurate for large-scale LS assessment within the Municipality of Rome. The ExtraTreesClassifier outperformed the others reaching an average F1-score of 0.896. Thereafter, we addressed the reliability of open-source LS maps at different scales of analysis (global to regional) by means of statistical and spatial analysis. The obtained results shed light on the difference in hazard zoning depending on the scale and mapping unit. An approach for low-resolution LS data fusion was attempted, assessing the importance of the adopted criteria, which increased the ability to detect occurred landslides while maintaining precision. [ABSTRACT FROM AUTHOR]

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation. A total of 322 landslide points were randomly divided into training (70%) and testing (30%) datasets. Nine causative factors were utilized for landslide susceptibility mapping (LSM). The importance of which was obtained using the information gain (IG) method. FR, IoV, WoE, and WLC were used to prepare the LSM using the training datasets and nine causative factors. Moreover, the accuracy and consistency were evaluated using AUC-ROC, precision, recall, overall accuracy (OA), balanced accuracy (BA), and F-score. The validation results showed that all methods performed well with the highest AUC and precision values of 0.913 and 0.95, respectively, for the IoV method, while the WLC method had the highest OA, BA, and F-score values of 0.808, 0.81, and 0.812, respectively. Finally, the results from LSM were used to evaluate the state of landslide susceptibility along the Imphal-Jiribam railway corridor. The results showed that 34% of the areas had high and very high susceptibility, while 40% were under less and significantly less susceptibility. The Tupul landslide area lay in medium susceptibility where the disastrous landslide occurred on 30 June 2022. Susceptibility values around the Noney and Khongsag railway station ranged from high to very high susceptibility. Thus, the study manifests the need for LSM preparation in rapidly constructing areas, which in turn will help the policymakers and planners for adopting strategies to minimize losses caused due to landslides. [ABSTRACT FROM AUTHOR]

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