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Precise and reliable displacement prediction is essential for preventing landslide disasters, but the evolution of landslides is a dynamic process influenced by diverse factors at different stages. Despite advances in the application of machine learning models to landslide displacement prediction, these models struggle to dynamically capture triggers during the prediction process. This limitation not only fails to capture the characteristics of the short-term fast deformation area, thus affecting the overall prediction accuracy, but also fails to establish a connection between the data relationships and the physical mechanism, thereby limiting the understanding of the physical mechanism of the landslide and resulting in low reliability of the prediction results. In this study, we establish a new model for landslide displacement prediction that combines double exponential smoothing (DES), variational mode decomposition (VMD), and evolutionary attention-based long short-term memory (EA–LSTM). The prediction process is as follows: (i) VMD is used to extract trend, periodic, and random displacement from cumulative displacement; (ii) DES is utilized for forecasting trend displacement, and periodic and random displacements are predicted by EA–LSTM; and (iii) these individual predictions are combined to produce the total displacement prediction. The proposed model is validated using monitoring data collected from the Baishuihe and Bazimen landslides in the Three Gorges Reservoir area. The results indicate that, compared with other models, the proposed model demonstrates higher predictive accuracy. In addition, the real-time dynamic weights of historical information revealed by the model on different time stamps are consistent with the actual historical evolution of landslides. These results verify that the proposed model is a promising tool for the high-quality prediction of landslides and can inform landslide treatment-related decision-making. [ABSTRACT FROM AUTHOR]

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Typhoon-induced debris flows pose a significant threat to the southeastern coastal regions of China. The existing typhoon-induced debris flow warning models, however, are largely limited in the refinement of their rainfall thresholds and in their lack of integration with geological factors. This study proposes a typhoon-induced debris flow warning model based on refined rainfall thresholds by integrating them with geological factors. Firstly, based on the characteristic rainfall of 159 debris flows in seven typhoon rainstorms between 1999 and 2019, the rainfall thresholds of debris flow under different geological conditions were obtained. Close relationships were noticed between rainfall thresholds and the number and density of debris flow disasters. The deterministic coefficient method (DCM) and sensitivity index were used to derive the weights of the geological factors, and the geological groups of the rock masses and vegetation type were the most important geological factors for debris flows. Finally, a typhoon-induced debris flow warning model was constructed, and refined rainfall thresholds were obtained. Due to different geological backgrounds, there were significant differences in the warning rainfall thresholds for debris flow disasters in different regions of the study area. The ROC indicator showed the high accuracy of this debris flow warning model. This research provides a scientific basis for the early warning and prediction of debris flows in typhoon-prone area of China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This scientometric analysis significantly advances the understanding of landslide risk assessment and economic losses, focusing on scientometric insights. This study aims at analyzing the global trends and structures of landslide risk and economic loss research from 2002 to 2023 using scientometric techniques such as co-authorship, co-word, co-citation, cluster analysis, and trend topics, among others. Thus, analysis of 92 studies gathered from Scopus and Web of Science databases reveals a continuous growth in environmental, social, and quantitative research topics. Predominant contributions hail mainly from China and Italy. The research identifies critical themes, including risk analysis, vulnerability, fragility, and economic losses. The current identified research combines advanced statistical methods, including logistic regression, with climate change scenarios and susceptibility assessments to reveal intricate connections between climatic shifts, hydrogeological hazards, and their economic and environmental impacts. This study provides researchers and practitioners with a comprehensive understanding of the status quo and research trends of ontology research landslide risk and its economic losses. It also promotes further studies in this domain. [ABSTRACT FROM AUTHOR]

On July 27, 2023, at approximately 04:00, a debris flow, with an estimated volume of 25,000 m3, struck Lanhua Village in the southern part of Shuicheng District, Guizhou Province, China. This event led to the burial and damage of 15 houses in Lanhua Village. Remarkably, residents successfully observed this debris flow 30 min before its peak arrival, enabling sufficient time for evacuation prior to the onset of this fatal geological disaster. To elucidate the evolutionary process of the Lanhua Village debris flow, a detailed field survey was conducted utilizing unmanned aerial vehicle (UAV) technology. The findings suggest that extreme heavy rainfall served as the primary trigger for the debris flow. Landslides, comprising residual slope deposits at the top of the slope and induced by rainfall, resulted in the obstruction of Highway X244 and the blockage of culverts beneath the road. These blockages contributed to the formation of a peak cross-basin flood, exceeding 7 m3/s, which acted as the driving force initiating the debris flow. The thick layer of colluvial deposits covering the slope surface, with approximately 23,000 m3 of material mobilized by flood erosion, constituted the primary source contributing to the rapid expansion and movement of the debris flow. The topography, characterized by steep upper slopes and gentler lower slopes, was a contributing factor to the debris flow deposits spreading into residential areas, resulting in disaster. The event in Lanhua Village exemplifies a "landslide-blockage-flood diversion-erosion-debris flow" disaster chain. [ABSTRACT FROM AUTHOR]

Rapid flow-like landslides frequently occur in mountainous regions. To mitigate the disasters caused by these landslides, it is crucial to develop robust numerical models that can accurately predict their run-out processes. Models based on the material point method (MPM) offer significant advantages in simulating large deformation issues in geomaterials, including landslides. However, applying these models to accurately simulate real-world rapid flow-like landslides remains a challenge, primarily due to the complexities involved in handling the three-dimensional (3D) sliding bed boundary. This paper introduces a novel 3D MPM model specifically designed for simulating rapid flow-like landslides that propagate across curved beds. The constraints of the sliding bed on the landslide are imposed by the boundary nodes close to the bed. These boundary nodes carry information about the normal vector of the sliding bed, derived directly from the digital terrain model (DTM). Furthermore, the model integrates a hybrid formulation that combines the Full Lagrangian Implicit Particle (FLIP) method with the Particle In Cell (PIC) method, facilitating a stable solution for the velocity and position of material points. The effectiveness of the proposed model is confirmed through a numerical analysis of a rigid block sliding down an inclined plane and an experiment of sand flow on a curved bed. The simulation results from these two benchmark scenarios align closely with both analytical and experimental data, attesting to the validity of the model. The model is then applied to analyze a rapid flow-like landslide that occurred in Gansu Province, China, characterized by a curved sliding bed. The outcomes illustrate the model's capability to efficiently capture the landslide's climbing and turning motions induced by the meandering topography. Moreover, it successfully reproduces the main deposition characteristics observed in the field, demonstrating the model's strong suitability for simulating the propagation of rapid flow-like landslides on naturally curved beds. [ABSTRACT FROM AUTHOR]

Landslides are widespread geomorphological phenomena with complex mechanisms that have caused extensive causalities and property damage worldwide. The scale and frequency of landslides are presently increasing owing to the warming effects of climate change, which further increases the associated safety risks. In this study, the relationship between historical landslides and environmental variables in the Hanjiang River Basin was determined and an optimized model was used to constrain the relative contribution of variables and best spatial response curve. The optimal MaxEnt model was used to predict the current distribution of landslides and influence of future rainfall changes on the landslide susceptibility. The results indicate that environmental variables in the study area statistically correlate with landslide events over the past 20 years. The MaxEnt model evaluation was applied to landslide hazards in the Hanjiang River Basin based on current climate change scenarios. The results indicate that 25.9% of the study area is classified as a high-risk area. The main environmental variables that affect the distribution of landslides include altitude, slope, normalized difference vegetation index, annual precipitation, distance from rivers, and distance from roads, with a cumulative contribution rate of approximately 90%. The annual rainfall in the Hanjiang River Basin will continue to increase under future climate warming scenarios. Increased rainfall will further increase the extent of high- and medium-risk areas in the basin, especially when following the RCP8.5 climate prediction, which is expected to increase the high-risk area by 10.7% by 2070. Furthermore, high landslide risk areas in the basin will migrate to high-altitude areas in the future, which poses new challenges for the prevention and control of landslide risks. This study demonstrates the usefulness of the MaxEnt model as a tool for landslide susceptibility prediction in the Hanjiang River Basin caused by global warming and yields robust prediction results. This approach therefore provides an important reference for river basin management and disaster reduction and prevention. The study on landslide risks also supports the hypothesis that global climate change will further enhance the frequency and intensity of landslide activity throughout the course of the 21st Century. [ABSTRACT FROM AUTHOR]

In order to study the technology of emergency disposal and provide insights into the landslide dam breach and rescue processes, a hyperbolic erosion dam breach model was used to predict the peak discharges of 10.11 and 11.03 Baige barrier lakes in the upper reach of the Jinsha River in northwest of China on October and November 2018. The results showed peak discharge values of 10,000 and 44,370 m3/s in the 10.11 and 11.03 Baige barrier lakes, respectively. Therefore, it is proposed to conduct a series of emergency disposals of the 11.03 barrier lake, including manual excavation of a drainage channel, demolition of the cofferdam at the Suwalong Hydropower Station under construction, and discharge of the Liyuan Hydropower Station Reservoir in operation. For the 11.03 barrier lake, the obtained results showed that: (1) the excavation of a 13.5 m deep drainage channel reduced the peak discharge by 24%, which is consistent with the predicted peak discharge of 34,400 m3/s. (2) The demolition of the cofferdam of the Suwalong Station under construction avoids superimposing and magnification of the flood peak. Indeed, in the absence of this measure, the peak discharge might be 1.6 times its original size. The demolition of the cofferdam reduced the impact of peak discharge on downstream areas. (3) The Liyuan Reservoir effectively decreased the peak discharge by 37%, thereby reducing the flood control pressure of the downstream cascade stations and minimizing the flood disaster-related losses through the joint dispatch of the cascade reservoirs. (4) The joint emergency response mechanisms, such as joint interministerial consultation, central-local government coordination, emergency response social mobilization, and joint dispatch of cascade reservoirs, were important and effective in the Baige landslide dams disposal. The present paper provides a systematic back analysis and further insights into the rescue process, key technologies, and emergency measures for the successive barrier lakes. It also provides a reference for emergency rescue of similar landslide dams and risk control of cascade hydropower stations in river basins. [ABSTRACT FROM AUTHOR]

There are a series of gentle Piedmont slopes with a unique special stratigraphic structure in Ningzhen area of China, which manifest as clay on the surface and the underlying being primarily gravel, exhibiting a binary structure similar to fluvial facies. The number of landslides has increased significantly on gentle Piedmont slopes in this area in recent years due to an increase in extreme rainfall events. Medium to large creeping landslides are dominant, and the sliding surface is between the clay and gravel layers. The main reason is the formation of a water cushion effect due to the unique stratigraphic structure. In this paper, based on previous research, we conducted a model test to verify the influence of the water cushion effect on gentle Piedmont slopes. Meanwhile, we derived a stability equation based on the static equilibrium for gentle Piedmont slopes under three conditions and proposed two methods to derive an early warning value of the pressure head. The rationality of the early warning value was verified by comparing it with measurements. The research results provide important theoretical and technical guidance for early warning and prediction of landslides on gentle Piedmont slopes. Practical Applications: The stability equation of landslides is crucial in adequately mitigating hazards, which can provide guidelines for landslide prone areas to avoid hazards in the future. Therefore, landslide warning and prediction is of the utmost significance to ensure the safety of human life, mitigate the negative impacts on the economy, and prevent landslide hazards in gentle Piedmont slopes. Government agencies, policymakers, local authorities, and urban planners can use stability equations and early warning values to plan effective landslide monitoring and prevention measures, and make wise decisions on land-use zoning and development. By identifying areas with high landslide susceptibility through early warning values, constructing buildings and engaging in human activities in hazardous zones can be avoided, reducing the risk of damage and loss during potential landslide events. In addition, landslide warning and prediction play a crucial role in disaster risk management of landslides in gentle Piedmont slopes. Authorities can use these warning values to prioritize areas for early measures. Residents can benefit from the results by being made aware of landslide-prone areas on their property. Consequently, this study is a scientific basis and technical support for the early warning and prediction of landslides on gentle Piedmont slopes in the Ningzhen area, China. [ABSTRACT FROM AUTHOR]

The selection of disaster-inducing factors and the construction of assessment models are two important stages in landslide susceptibility assessment. Existing research has neglected the influence of time-varying dynamic factors on landslide susceptibility. On the contrary, the best assessment model that fits the characteristics of disaster-pregnant environment in a certain area needs to be determined through a large number of comparative studies. In this paper, Boshan District, China was taken as the research area, and the assessment factor combination of static disaster-inducing factors and the change value of dynamic disaster-inducing factors in the current year was constructed. Landslide susceptibility modeling was carried out by random forest (RF), logistic regression (LR), support vector machine (SVM), stacking ensemble and convolutional neural network (CNN), and the assessment model with the highest accuracy was determined based on the area under the curve (AUC) method. Based on the geodetector, the interpretation degree of land use to landslide susceptibility and the relationship between the change information of dynamic disaster-inducing factors and the spatial distribution of landslide susceptibility were analyzed. The results show that CNN outperforms other models in the performance of modeling, with an AUC value of 0.920. The extremely high sensitive zones for landslides in the Boshan District were mainly distributed in the northwest, south, and east regions. The overall explanatory power of land use on landslide susceptibility shows a declining trend, suggesting that the spatial layout of land use in the Boshan District has been optimized. The rapidly growing population has destroyed the geological and hydrological environment and increased the probability of landslide susceptibility; Multiple acute changes of the normalized difference vegetation index (NDVI) in the opposite direction were more likely to trigger landslides, so it is not suitable to carry out logging, planting and other work repeatedly in a short time. This research can provide a theoretical foundation for land use planning and landslide prevention policies in the Boshan District. [ABSTRACT FROM AUTHOR]

Liangshan Prefecture, located at the northeastern edge of the Hengduan Mountain System and within the southern section of the Sichuan-Yunnan tectonic belt in Sichuan Province, China, a region prone to landslides, collapses and debris flows due to its active tectonics, complex topography and significant river erosion. By analysing a dataset of environment factors and geological hazard catalogue, the research uses the Relief algorithm to identify critical influencing factors for each hazard type, selecting 10, 9 and 9 factors for landslides, collapses and debris flows, respectively. Five models are used to assess the vulnerability of these hazards: the Information Value model, the Evidence Weight model, the Logistic Regression model, and both the Evidence Weight-Logistic Regression and the Information Value-Logistic Regression coupled models. The effectiveness of these models is confirmed by confusion matrix and ROC curve analyses, with the combined models showing particularly high accuracy in assessing susceptibility. High risk zones were identified in specific areas and along major fault zones in Liangshan Prefecture. The research provides significant insights into the susceptibility of geological hazards in mountainous and canyon regions, offering a comprehensive approach that goes beyond the limitations of single model applications. This methodology not only provides more accurate and comprehensive results, but also serves as a fundamental reference for geological hazard mitigation and management in Liangshan Prefecture, potentially benefiting similar regions worldwide. [ABSTRACT FROM AUTHOR]

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500–3700 m with slope angles of 10–30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures. [ABSTRACT FROM AUTHOR]

A seismic-induced landslide is a common geological catastrophe that occurs in nature. The Wangjiayan landslide, which was triggered by the Wenchuan earthquake, is a typical case in point. The Wanjiayan landslide caused many casualties and resulted in enormous property loss. This study constructs a simple surficial failure model based on the upper bound approach of three-dimensional (3D) limit analysis to evaluate the slope stability of the Wangjiayan case, while a traditional two-dimensional (2D) analysis is also conducted as a reference for comparison with the results of the 3D analysis. A quasi-static calculation is used to study the effect of the earthquake in terms of horizontal ground acceleration, while a parametric study is conducted to evaluate the critical cohesion of slopes. Rather than employing a 3D analysis, using the 2D analysis yields an underestimation regarding the safety factor. In the Wangjiayan landslide, the difference in the factors of safety between the 3D and 2D analyses can reach 20%. The sliding surface morphology as determined by the 3D method is similar to actual morphology, and the parameters of both are also compared to analyze the reliability of the proposed 3D method. [ABSTRACT FROM AUTHOR]

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%. [ABSTRACT FROM AUTHOR]

A non-periodic "step-like" variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. [ABSTRACT FROM AUTHOR]

Nonlinear deformation is a dynamically changing pattern of multiple surface deformations caused by groundwater overexploitation, underground coal mining, landslides, urban construction, etc., which are often accompanied by severe damage to surface structures or lead to major geological disasters; therefore, the high-precision monitoring and prediction of nonlinear surface deformation is significant. Traditional deep learning methods encounter challenges such as long-term dependencies or difficulty capturing complex spatiotemporal patterns when predicting nonlinear deformations. In this study, we developed a dual-attention-mechanism CNN-LSTM network model (DACLnet) to monitor and accurately predict nonlinear surface deformations precisely. Using advanced time series InSAR results as input, the DACLnet integrates the spatial feature extraction capability of a convolutional neural network (CNN), the advantages of the time series learning of a long short-term memory (LSTM) network, and the enhanced focusing effect of the dual-attention mechanism on crucial information, significantly improving the prediction accuracy of nonlinear surface deformations. The groundwater overexploitation area of the Turpan Basin, China, is selected to test the nonlinear deformation prediction effect of the proposed DACLnet. The results demonstrate that the DACLnet accurately captures developmental trends in historical surface deformations and effectively predicts surface deformations for the next two months in the study area. Compared to traditional LSTM and CNN-LSTM methods, the root mean square error (RMSE) of the DACLnet improved by 85.09% and 68.57%, respectively. These research results can provide crucial technical support for the early warning and prevention of geological disasters and can serve as an effective alternative tool for short-term ground subsidence prediction in areas lacking hydrogeological and other related data. [ABSTRACT FROM AUTHOR]

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability. [ABSTRACT FROM AUTHOR]

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models. [ABSTRACT FROM AUTHOR]

On August 12, 2015, a catastrophic landslide occurred in Shanyang County, Shaanxi Province, China, resulting in 7 deaths and 53 missing. This study investigates the lifecycle evolution and failure mechanism of the Shanyang landslide with multi-source remote sensing data, emphasizing the critical role of locked segments in the Shanyang landslide. Differential interferometric analysis and deformation decomposition were utilized to reveal the pre-failure deformation pattern of the Shanyang landslide. Creeping deformation was found along the underlying soft layer 4 months prior to the landslide, with the deformation mainly occurring downslope and controlled by the locked segment at the front edge of the slope. The integration of a 1:1000 pre-failure topographic map and a high-precision post-failure digital elevation model determined the landslide volume to be 1.60 × 106 m3 and revealed a maximum travel distance of 500 m. Combining engineering geological zoning with deformation data, the Shanyang landslide was classified as a typical locked-segment-dominated slide in soft-hard interbedded strata, with rainfall as a key deformation influence factor. Finally, using the time series deformation from SBAS-InSAR, the post-failure stability of the landslide area was analyzed. This study demonstrates the potential of integrating multi-temporal remote sensing techniques to identify the entire deformation and destruction process of landslides and their influencing factors, which offers valuable insights for improving early landslide warnings and hazard assessments. [ABSTRACT FROM AUTHOR]

On December 4, 2020, a massive high-locality landslide occurred at Qingliu Village, Li County, Sichuan Province, China (UTC + 8). This event resulted in a landslide deposit of approximately 34.8 × 104 m3, causing the destruction of two roads and several houses on the hillslope and blocking a downstream river. Extensive field investigations, UAV photography, borehole drilling, and GNSS monitoring were conducted after the landslide. Additionally, a PFC3D numerical simulation was used to reproduce the movement of the landslide after calibrating its meso-strength parameters. Based on the motion and deposit characteristics, the landslide is divided into a major sliding zone (comprising the source zone, impact-shoveling zone, and deposition zone) and a landslide-affected zone. The primary triggering factor for the Qingliu landslide is the slope mass damage caused by frequent preceding earthquakes. On the other side, the direct triggers are continuous rainfall and upslope meltwater, which increased the weight and further weakened the strength of the slope mass. The landslide initiation involves the formation of a tensile crack in the major sliding zones, followed by the shoveling of loose debris by the displaced landslide mass, and then termination with the striking at the slope foot and damming a river. This study offers valuable insights into the evolutionary processes of high-locality landslides. [ABSTRACT FROM AUTHOR]

Landslides caused by typhoons and rainstorms frequently occur in the mountainous areas of southeastern China, with a complex mechanism of disaster generation. Therefore, establishing a prediction model for early warning against such landslides is of great theoretical and practical significance. In response to the current shortcomings in landslide displacement prediction models, considering the dynamic evolutionary characteristics of landslide physical mechanisms in the study area, a novel model based on Bayesian optimization-temporal convolutional networks was developed. The proposed model can automatically perform feature extraction on a dataset of complex multivariate time series, preventing leakage of future data during the training process. Simultaneously, it leverages Bayesian optimization to discover optimal hyperparameters within the model, thereby offering additional insights into the hyperparameter tuning process. Compared with recurrent neural networks, the proposed model with flexible receptive fields has faster training speed and parallel computing capability. Finally, we experimentally compared the performance of the proposed algorithm and other common algorithms by analyzing the monitoring data from the Yaoshan landslide disaster in Anxi County, Fujian Province, China. The results show that the proposed model yields the best prediction results in various prediction ranges. [ABSTRACT FROM AUTHOR]

Machine learning models are widely used for landslide susceptibility prediction (LSP). To better develop a landslide susceptibility prediction model for the Loess Plateau, this study compared different machine-learning models (Logistic regression (LR), support vector machines (SVM), random forests (RF), Naïve Bayes (NB), and multilayer perceptron (MLP)). Using Xunyi County, China as the study area, 1096 landslides were collected and divided into a training set (70%) and a test set (30%). Significant influencing factors were selected from 20 landslide factors using GeoDetector. The relationships between these factors and landslides were determined by normalized frequency ratios. Five trained machine learning models calculated landslide susceptibility indices and generated landslide susceptibility maps. Additionally, the accuracy (ACC), precision (PRE), recall (REC), F1 score, AUC values, LSI distribution characteristics, and overlay analysis of historical landslides with LSM were used to evaluate the performance of the models. The results show that the LSP results of the five models are generally reasonable. The results further indicated that the use of GeoDetector was able to filter out the main factors of eolian landslides, improving the predictive performance of the model and obtaining better LSMs. The RF model had the best overall performance, followed by the SVM, and the MLP model created LSM high susceptibility zones that could contain the highest number of historical landslides. The q-values of factor contributions indicate that elevation, NDVI, rainfall, POI kernel density, and RDLS are the main factors controlling landslides, which need to be paid attention to for landslide control in the Loess Plateau region. The results of this study can provide a reference for the follow-up study of landslide prevention and control, especially landslide susceptibility modeling in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Debris-flow grain-size distributions (GSDs) control runout length and mobility. Wide, bimodal GSDs and those containing a higher proportion of silt and clay have been shown experimentally to increase runout length. However, the relationship between grain size and mobility has not been well established in field conditions. Here, we compared the grain-size characteristics of two debris flows with considerably different runout lengths (1.5 km vs. 8 km) to understand the role of grain size in governing runout. The two debris flows were triggered in the same rainfall event from coseismic landslide debris generated in the 2008 Wen-chuan earthquake in catchments with similar lithology and topography. We compared the deposited GSDs and their spatial patterns using our rare, three-dimensional GSD datasets. Surprisingly, the proportions of each size fraction deposited by the two flows were statistically indistinguishable. The spatial pattern of grain size differed between the two flows, with evidence of inverse grading only preserved in the smaller deposit. From these observations, we can infer that the GSDs of both flows were determined by the coseismic landslide source material, and that there was little difference in the GSDs of material entrained as the flows bulked. The contrasting spatial distributions of grains indicated that different internal processes were dominant within the two flows. These findings demonstrate that where GSDs are dominated by coarse grains and are governed by similar source conditions, grain size plays a lesser role relative to sediment supply and hydrology in controlling the runout length of large catastrophic post-earthquake debris flows. [ABSTRACT FROM AUTHOR]

Geological events that cause casualties and huge property losses frequently occur in the hilly region of Southwest China. Especially for economically underdeveloped mountainous cities, the vulnerability of social and ecological systems greatly affects urban development. Therefore, it is necessary to conduct multi-hazard exposure research in mountain areas. The importance of this study is that the five machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) are first used to develop multi-hazard (specifically landslides and rockfalls) susceptibility map in three typical hilly areas in the Southwest China (Qijiang, Chuishui and Wuxi). The performances of the models are then compared using the F1-Score and the true skill score (TSS) respectively. Finally, the multi-hazard exposure maps of each district are developed by combining multi-hazard susceptibility and exposure factors. According to the results of this study, it can be found that the LightGBM and CatBoost algorithms are suitable for the development of landslide and rockfall hazard susceptibility maps in the hilly region of southwest China, and this type of gradient boosting algorithms have good development prospects. In addition, in future research on geological hazards in this hilly area, more attention should be paid to conditioning factors such as rainfall, altitude, slope, distance from the faults, and lithology. The results of this study can provide reference for the screening of landslide and rockfall conditioning factors in such mountainous areas, and can also provide certain support for the planning of the social ecosystem in mountainous areas and the formulation of natural hazard mitigation measures. [ABSTRACT FROM AUTHOR]

In the previous decades, loess landslides with amount of 1992 occurred in Yili valley, Xinjiang autonomous region of China. These landslides have caused a series of engineering disasters. However, comprehensive report regarding the characteristics of these landslides has not been made, and the sliding mechanism is also not thoroughly clarified. In order to address these issues, extensive field surveys and laboratory tests have been conducted. These results conclude that the length/width, sliding thickness, altitude and the gradient of these loess landslides mostly locate in the range of 10–300 m, 0.5–20 m, 1000–2500 m and 10°–50° respectively. Additionally, these loess landslides in Yili belong to the snow-melt landslides, which primarily occurred in the snow-melt season from yearly mid-February to mid-May. For these snow-melt landslides, the sliding mechanism includes following parts: freeze-thawing action notably weakens the shear strength of superficial loess, resulting in the sliding of superficial loess layer. Moreover, freeze-thawing also promotes the inner-fissures growth in superficial loess, increasing its hydraulic conductivity and accelerating the infiltration of melted snow-water or rainfall into lower loess layer. As lower loess becomes saturated, its strength diminishes significantly, further inducing the deep sliding. Highlights: Loess landslide in Yili occurred at shallow location and surrounded by fissure. Loess landslides in Yili valley belong to snow-melt landslides. Freeze-thawing action induce the sliding of superficial loess layer. Freeze-thawing accelerates infiltration of melted snow-water or rainfalls. Humidification action induces the deep-sliding of snow-melt landslides. [ABSTRACT FROM AUTHOR]

Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved substantial success in landslide risk management applications. However, the complexity of classically trained ML is often beyond nonexperts. With the rapid growth of practical applications, an "off-the-shelf" ML technique that can be easily used by nonexperts is highly relevant. In the present study, a new paradigm for an end-to-end ML modeling was adopted for LSM in the Three Gorges Reservoir area (TGRA) using automated machine learning (AutoML) as the backend model support for the paradigm. A well-defined database consisting of data from 290 landslides and 11 conditioning factors was collected for implementing AutoML and compared with classically trained ML approaches. The stacked ensemble model from AutoML achieved the best performance (0.954), surpassing the support vector machine with artificial bee colony optimization (ABC-SVM, 0.931), gray wolf optimization (GWO-SVM, 0.925), particle swarm optimization (PSO-SVM, 0.925), water cycle algorithm (WCA-SVM, 0.925), grid search (GS-SVM, 0.920), multilayer perceptron (MLP, 0.908), classification and regression tree (CART, 0.891), K-nearest neighbor (KNN, 0.898), and random forest (RF, 0.909) in terms of the area under the receiver operating characteristic curve (AUC). Notable improvements of up to 11% in AUC demonstrate that the AutoML approach succeeded in LSM and could be used to select the best model with minimal effort or intervention from the user. Moreover, a simple model that has been customarily ignored by practitioners and researchers has been identified with performance satisfying practical requirements. The experimental results indicate that AutoML provides an attractive alternative to manual ML practice, especially for practitioners with little expert knowledge in ML, by delivering a high-performance off-the-shelf solution for ML model development for LSM. [ABSTRACT FROM AUTHOR]

The Baihetan Hydropower Station reservoir area began impoundment in 2021, triggering the reactivation of ancient landslides and the formation of new ones. This not only caused direct landslide disasters but also significantly increased the likelihood of secondary surge wave disasters. This study takes the Wangjiashan (WJS) landslide in the Baihetan reservoir area as an example and conducts large-scale three-dimensional physical model experiments. Based on the results of the physical model experiments, numerical simulation is used as a comparative verification tool. The results show that the numerical simulation method effectively reproduces the formation and propagation process of the WJS landslide-induced surge waves observed in the physical experiments. At the impoundment water level of 825 m, the surge waves generated by the WJS landslide pose potential threats to the Xiangbiling (XBL) residential area. In this study, the numerical simulation based on computational fluid dynamics confirmed the actual propagation forms of the surge waves, aligning well with the results of the physical experiments at a microscopic scale. However, at a macroscopic scale, there is some discrepancy between the numerical simulation results and the physical experiment outcomes, with a maximum error of 25%, primarily stemming from the three-dimensional numerical source model. This study emphasizes the critical role of physical model experiments in understanding and mitigating surge wave disasters in China. Furthermore, physical experiments remain crucial for accurate disaster prediction and mitigation strategies. The theories and methods used in this study will provide important references for future research related to landslide disasters in reservoir areas. [ABSTRACT FROM AUTHOR]

Accurate dynamic modeling of landslides could help understand the movement mechanisms and guide disaster mitigation and prevention. Discontinuous deformation analysis (DDA) is an effective approach for investigating landslides. However, DDA fails to accurately capture the degradation in shear strength of rock joints commonly observed in high-speed landslides. In this study, DDA is modified by incorporating simplified joint shear strength degradation. Based on the modified DDA, the kinematics of the Baige landslide that occurred along the Jinsha River in China on 10 October 2018 are reproduced. The violent starting velocity of the landslide is considered explicitly. Three cases with different violent starting velocities are investigated to show their effect on the landslide movement process. Subsequently, the landslide movement process and the final accumulation characteristics are analyzed from multiple perspectives. The results show that the violent starting velocity affects the landslide motion characteristics, which is found to be about 4 m/s in the Baige landslide. The movement process of the Baige landslide involves four stages: initiation, high-speed sliding, impact-climbing, low-speed motion and accumulation. The accumulation states of sliding masses in different zones are different, which essentially corresponds to reality. The research results suggest that the modified DDA is applicable to similar high-level rock landslides. [ABSTRACT FROM AUTHOR]

The Loess Plateau is marked by intense neotectonic activity and frequent earthquakes. Its unique physico-mechanical properties, combined with the granular overhead pore structure of loess, render it prone to seismic landslides triggered by strong earthquakes. Different types of loess seismic landslides have distinct formation mechanisms, disaster-causing characteristics, and risk assessment programs. In this study, the risk of seismic-collapsed loess landslides as one of the types of loess seismic landslides was evaluated on the Loess Plateau. A risk zoning map for seismic-collapsed loess landslides on the Loess Plateau, considering various exceedance probabilities, was compiled by assessing eight factors. These factors include peak ground acceleration, microstructure of loess, and were evaluated using both the minimum disaster-causing seismic peak ground acceleration zoning method and the analytic hierarchy process. The following conclusions were obtained: (1) Earthquakes are the primary inducing factor for seismic-collapsed loess landslides, with other factors serving as influencers, among which the microstructure of loess carries the highest weight; (2) Across various exceedance probabilities, the likelihood of seismic-collapsed loess landslides occurring at 63% of the 50-year exceedance probability is low. Moreover, as the minimum hazard-causing seismic peak ground acceleration increases, the risk of occurrence of seismic-collapsed loess landslides rises, leading to a gradual expansion of the area share in moderate and high-risk zones; (3) Hazard evaluation results align well with existing data on seismic-collapsed loess landslides and findings from field investigations. The case of seismic-collapsed loess landslides induced by the M6.2 magnitude earthquake in Jishishan County, China, is presented as an illustration. The combined use of the minimum hazard-causing seismic peak ground acceleration zoning method and the analytic hierarchy process method offers a reference for geohazard hazard assessment, with earthquakes as the primary inducing factor and other factors as influencers. [ABSTRACT FROM AUTHOR]

The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE's powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant "8–31" 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies. [ABSTRACT FROM AUTHOR]

The frequent occurrence of landslides poses a serious threat to people's lives and property. In order to evaluate disaster hazards based on remote sensing images via machine learning means, it is essential to establish an image database with manually labeled boundaries of landslides. However, the existing datasets do not cover diverse types of mountainous landslides. To address this issue, we propose a high-resolution (1 m) diverse mountainous landslide remote sensing dataset (DMLD), including 990 landslide instances across different terrain in southwestern China. To evaluate the performance of the DMLD, seven state-of-the-art deep learning models with different loss functions were implemented on it. The experiment results demonstrate not only that all of these deep learning methods with different characteristics can adapt well to the DMLD, but also that the DMLD has potential adaptability to other geographical regions. [ABSTRACT FROM AUTHOR]

Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain. [ABSTRACT FROM AUTHOR]

Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

A large number of laboratory investigations related to the permeability have been conducted on the sliding zones. Yet little attention has been paid to the particular sliding zones of the slide-prone Badong Formation. Here, we experimentally investigate the permeability nature and the mechanism of seepage in the viscous sliding zone of the Huangtupo Landslide. Saturated seepage tests have been performed first with consideration of six dry densities and thirteen hydraulic gradients, in conjunction with the mercury intrusion porosimetry test and scanning electron microscopy test for the microstructure analysis after seepage. The results show that seepage in the sliding zone soil does not follow Darcy's Law, since there is a threshold hydraulic gradient (i0) below which no flow is observed and a critical hydraulic gradient (icr) over which the hydraulic conductivity (K) tends to be stable. The percentage of bound water could be responsible for the occurrence of i0 and icr. Furthermore, pore size distributions (PSD) less than 0.6 µm and between 10 and 90 µm exhibit positive and negative correlations with the i0, respectively, indicating that the i0 is related to the PSD. The mechanism accounting for this result is that pore water pressure forces fine clay particles into the surrounding large pores and converts arranged particles to discretely distributed ones, thereby weakening the connectivity of pores. The seepages in the sliding zones behave differently from that in the sliding mass and sliding bed in response to the permeability. [ABSTRACT FROM AUTHOR]

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively. [ABSTRACT FROM AUTHOR]

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation). [ABSTRACT FROM AUTHOR]

In recent years, several catastrophic landslide events have been observed throughout the globe, threatening to lives and infrastructures. To minimize the impact of landslides, the need of landslide susceptibility map is important. The study aims to extract high-quality non-landslide samples and improve the accuracy of landslide susceptibility modelling (LSM) outcomes by applying a coupled method of ensemble learning and Machine Learning (ML). The Zigui-Badong section of the Three Gorges Reservoir area (TGRA) in China was considered in the present study. Twelve influencing factors were selected as inputs for LSM, and the relationship between each causal factor and landslide spatial development was quantitatively analyzed. A total of 179 landslides have been used in the present study. About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. Logistic Regression (LR) model was applied to produce an initial susceptibility map, and the non-landslide samples were selected within the classified low-susceptibility zone. Subsequently, two ML classifiers - the Classification and Regression Tree (CART), and the Multi-Layer Perceptron (MLP), and four coupling models - the CART-Bagging, CART-Boosting, MLP-Bagging, and MLP-Boosting, were utilized for LSM. Finally, the receiver operating characteristics (ROC) curve and statistical analysis were applied for accuracy assessment. The results show that altitude and distance to rivers were the main causal factors of landslides in the study area. The LR-MLP-Boosting performed the best with an accuracy of 0.986 followed by the LR-CARTBagging, LR-CART-Boosting, and LR-MLP-Bagging. Accuracy comparisons demonstrate that ensemble learning algorithm can notably enhance the LSM performance of ML classifiers, and the Boosting algorithm marginally outperforms the Bagging algorithm. Moreover, the LR model can effectively constrain the selection range of non-landslide samples. The non-landslide sampling method constrained by LR yields higher quality samples compared to raditional random sampling method with no constraints, which develops a more excellent LSM. [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

Landslides represent one of the most frequent and destructive natural hazards in recent years. In highly seismic countries, the occurrence of large earthquakes is a significant triggering factor in the generation of these landslides. Therefore, it is of interest to various disciplines within civil engineering to study these phenomena through empirical analysis, analytical methods, and numerical modelling, aiming to provide a more accurate representation of these complex phenomena. For this purpose, a computational modelling approach is developed to describe the dynamics of a landslide or rockslide induced by seismic loading, using the Material Point Method (MPM). Presently, the utilization of MPM holds considerable significance because it is a numerical method engineered to simulate large deformations. This stands in contrast to conventional methods like the Finite Element Method (FEM), which struggles to precisely deal with this type of problems due to the generation of errors related to mesh distortion. In this study, it is performed a modelling process involving a real and documented scenario--a massive landslide occurrence in the vicinity of Daguangbao, China, triggered by the 2008 Wenchuan Earthquake. The obtained results successfully capture the landslide dynamics in terms of velocities, deformations, and travel distances in accordance with existing reports and other research endeavours. The maximum attained velocities of the landslide are approximately 100 km/h, affirming the catastrophic nature of this event. [ABSTRACT FROM AUTHOR]

On September 5, 2022, a strong earthquake with a magnitude of MS6.8 struck Luding County in Sichuan Province, China, triggering thousands of landslides along the Dadu River in the northwest-southeast (NW-SE) direction. We investigated the reactivation characteristics of historical landslides within the epicentral area of the Luding earthquake to identify the initiation mechanism of earthquake-induced landslides. Records of the two newly triggered and historical landslides were analyzed using manual and threshold methods; the spatial distribution of landslides was assessed in relation to topographical and geological factors using remote sensing images. This study sheds light on the spatial distribution patterns of landslides, especially those that occur above historical landslide areas. Our results revealed a similarity in the spatial distribution trends between historical landslides and new ones induced by earthquakes. These landslides tend to be concentrated within a range of 0.2 km from the river and 2 km from the fault. Notably, both rivers and faults predominantly influenced the reactivation of historical landslides. Remarkably, the reactivated landslides are characterized by their small to medium size and are predominantly situated in historical landslide zones. The number of reactivated landslides surpassed that of previously documented historical landslides within the study area. We provide insights into the critical factors responsible for historical landslides during the 2022 Luding earthquake, thereby enhancing our understanding of the potential implications for future co-seismic hazard assessments and mitigation strategies. [ABSTRACT FROM AUTHOR]

Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China's WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

On October 5, 2021, a landslide-debris flow disaster chain occurred suddenly in Hanping village, Shaanxi Province, China. This catastrophic disaster chain damaged 7 houses, 41.9 hectares of arable land and 3 roads and resulted in 1 death. Based on a detailed field investigation of the disaster site, we analyzed the dynamic evolution of the disaster chain by using experimental analysis, unmanned aerial vehicle photogrammetry, satellite remote sensing interpretation and the SBAS-InSAR technique and then preliminarily revealed the movement process and causal mechanism of the disaster chain. The results suggested that the first landslide initiated in the upper part of Canger cliff, which is the result of the combined effects of slope structure, earthquake damage, engineering disturbance, and rainfall infiltration. Among them, extreme rainfall events are the primary factors that induce landslides. Before the landslide, InSAR results showed that deformations had already appeared in the source area, and the deformation rate had a strong correlation with precipitation. Then, the potential-to-kinetic transformation effect and air cushion effects generated by the landslide movement in the narrow and steep section of Canger cliff led to the disintegration of the sliding body. With replenished surface runoff, the clastic flow gradually transformed into debris flow. Moreover, due to the dam-breaching effects at bayonets and bends and the entrainment effect of the high-density debris flow along the gully, the scale of debris flow increases gradually, resulting in catastrophic damage during the movement. The findings of this study provide a significant reference and guidance for understanding the chain-generation mechanism of landslide-debris flow disaster chains, as well as informing disaster prevention and mitigation strategies. [ABSTRACT FROM AUTHOR]

Landslide is a frequent geological hazard in the Three Gorges reservoir area of China. Impulse waves could be created and cause substantial damage if the landslide collides with a water body. A nonlinear shallow water equation in combination with a progressive landslide model is proposed to simulate this catastrophic disaster. The proposed approach is distinguished by rapid calculation, less computational burden, and high practical value. By simulating the 2008 Three Gorges Reservoir Gongjiafang landslide and the subsequent impulse waves, we illustrated and validated this methodology. The initiation and motion processes of the landslide, as well as the generation, propagation, and run-up processes of impulse waves, were thoroughly performed. The simulation results were in good agreement with the field survey data, demonstrating the viability of the proposed approach for simulating the impulse waves triggered by landslide events. This numerical approach is beneficial for engineers to perform a preliminary estimation of landslide-generated waves and further facilitate the prevention and mitigation of geological hazards. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the mechanisms and kinematic characteristics of landslides in the southwestern mountainous regions of China. Field surveys and aerial photography were conducted to establish a discrete element method model with calibrated mesomechanical parameters. By considering internal and external triggering factors, a coupled finite difference and discrete element method was used to simulate the deformation, failure mechanism and movement process of slopes as well as to analyse the characteristics of debris movement. The findings revealed that the unique lithological and unfavourable geological structures resulted in an extremely fragmented rock mass, which was the internal cause of landslides. The main external cause was the mining of coal seams beneath the slumped mass, leading to extensive cracks at the trailing edge of the slope and subsequent debris movement. The average velocity of the slumped mass exhibited fluctuation before 3.5 s, followed by a rapid increase at 3.5 s, reaching a maximum speed of approximately 9.5 m/s at 9.8 s. The velocity and displacement of the debris gradually decreased from the bottom to the top in the sliding source area. The maximum displacement of the debris was 1.24 km. The entire landslide process lasted approximately 47 s, with the debris accumulating mostly at the toe of the slope. The research findings provide valuable theoretical support for the prevention and control of geological hazards induced by underground mining activities. [ABSTRACT FROM AUTHOR]

Loess area is the region that geohazards happened most frequently, accounting for 1/3 of the geohazards in China. Cutting slope has become the most prominent products in human engineering activities and slope failure induced by rainfall has become the main form in recent years. Well-instrumented centrifuge model tests have been introduced to investigate the failure process and failure pattern, including pore water pressure, progressive deformation-failure process and characteristic of the high cutting slope by rainfall. The results show that rainfall induced loess slope failure is characterized by shallow slide to flow and two deeper creepage sliding-tension surfaces. All the sliding faces are characterized by planar surfaces parallel to slope surface. The planar sliding surface differs a lot to the circular sliding surface in the gravitational soil landslide. The accumulative deformation especially the abrupt displacement before failure induced the excess pore water pressure, after which flow failure with high-speed happened. The pore-water transducers on both sides of the shallow sliding surface have distinct response to slope deformation. The quantitative monitoring data indicates that the liquefaction is not the reason but the result of the deformation accumulation and big transient deformation. [ABSTRACT FROM AUTHOR]

Introduction: Landslide-dammed lake outburst floods (LLOFs) are common natural disasters in high-mountain regions, posing serious safety threats to residents' livelihoods and properties and causing major damage to engineering facilities. Giant landslides and river damming events commonly occur in the Eastern Tibetan Plateau in southwestern China. Dam failure generate LLOFs that form outburst deposits (ODs). This phenomenon is particularly common in some giant ancient landslide-dammed lakes. Methods: This study conducted a detailed investigation of the sedimentary characteristics of large-scale landslide-dammed lake outburst deposits in the Diexi Reach of the Upper Minjiang River and Tangjiashan Reach of the Tongkou River Basin, West Sichuan Plateau, China. Meanwhile, typical evidence of highenergy ODs was recorded. Results and Discussion: The longitudinal distribution of these ODs is similar to an elongated fan-shaped terrace along the river channel, presenting a distinctive sedimentary disordered -- sub-ordered -- ordered sequence from upstream to downstream. Several typical units of "sedimentary facies" are developed in the OD profiles, such as boulder units deposited by high-energy outburst flood (OF) events and the gravel and sand units representing pulsating-flow sedimentary environments during the recession stage. The grain size frequency curves are bimodal, and the granularity accumulated curves are upward convex, which reflect that the detrital characteristics of the sediment source area are mainly composed of coarse gravel and boulders. This indicates that the coarse gravel sediment gradually become decreased from upstream to downstream. Moreover, the OD hydrodynamic intensity displays a gradual weakening, and sediment sorting is improved. From upstream to the downstream, the mean particle-size and sorting of the ODs gradually decrease. The skewness become larger, and the kurtosis of the ODs is distributed in all the types. In addition, the different combinations of quartz sand surface microtextures indicate the transformation from high-to low-energy impacts over a short distance, which is controlled by flood hydrodynamics and regime. Significance: These sedimentary characteristics of ODs explain the hydrodynamic changes during the propagation of OFs, and are also important records for distinguishing between ODs, and "normal" floods. [ABSTRACT FROM AUTHOR]

The Jishishan Ms 6.2 earthquake occurred at 23:59 on December 18, 2023 in Gansu Province, China. We conducted a field survey to assess the hazards and damages caused by the earthquake and its associated geo-activities. Subsequently, we organized a seminar to discuss the possible causes of the destruction of a prehistoric site—Lajia Settlement—dated back to four thousand years B.P. and located only several kilometers away from the epicenter of the Jishishan earthquake. The Jishishan earthquake was unique for its hazard and disaster process, which featured ground shaking and a series of complex geological and geomorphological activities: sediment and soil spray piles, liquefaction, collapse, landslide, and mudflow along water channels. We define this phenomenon as the Jishishan earthquake ripple hazard (JERH). The most recent evidence from the JERH suggests that a prehistoric earthquake similar to the JERH, instead of riverine floods or earthquake-induced landslide dam outburst flood, as previously hypothesized, destroyed the Lajia Settlement. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are expected to produce more intense precipitation under global warming. However, substantial uncertainties exist in the projection of coarse-resolution global climate models. Here, we use deep learning to aid targeted cloud-resolving simulations of extreme TCs. Contrary to the Clausius-Clapeyron (CC) scaling, which indicates a 7% moisture increase per K warming, our simulations reveal more complex responses of TC rainfall. TCs will not intensify via strengthened updrafts but through the expansion of deep convective cores with suppression of shallow cumulus and congestus. Consequently, while localized hourly rainfall may adhere to the CC scaling, precipitation accumulation over city-sized areas could surge by 18%K-1. This super-CC intensification due to changing TC structure has profound implications for floods and landslides. Estimations using Hong Kong's slope data confirm this concern and suggest an up to 215% increase in landslide risks with 4-K warming, highlighting amplified threats from compound disasters under climate change. [ABSTRACT FROM AUTHOR]