Search Results (814)

Landslides frequently occur in the Xinjiang Uygur Autonomous Region of China due to its complex geological environment, posing serious risks to human safety and economic stability. Existing studies widely use machine learning models for landslide susceptibility prediction. However, they often fail to capture the threshold and interaction effects among environmental factors, limiting their ability to accurately identify high-risk zones. To address this gap, this study employed a gradient boosting decision tree (GBDT) model to identify critical thresholds and interaction effects among disaster-causing factors, while mapping the spatial distribution of landslide susceptibility based on 20 covariates. The performance of this model was compared with that of a support vector machine and deep neural network models. Results showed that the GBDT model achieved superior performance, with the highest AUC and recall values among the tested models. After applying clustering algorithms for non-landslide sample selection, the GBDT model maintained a high recall value of 0.963, demonstrating its robustness against imbalanced datasets. The GBDT model identified that 8.86% of Xinjiang’s total area exhibits extremely high or high landslide susceptibility, mainly concentrated in the Tianshan and Altai mountain ranges. Lithology, precipitation, profile curvature, the Modified Normalized Difference Water Index (MNDWI), and vertical deformation were identified as the primary contributing factors. Threshold effects were observed in the relationships between these factors and landslide susceptibility. The probability of landslide occurrence increased sharply when precipitation exceeded 2500 mm, vertical deformation was greater than 0 mm a⁻¹, or the MNDWI values were extreme (<−0.4, >0.2). Additionally, this study confirmed bivariate interaction effects. Most interactions between factors exhibited positive effects, suggesting that combining two factors enhances classification performance compared with using each factor independently. This finding highlights the intricate and interdependent nature of these factors in landslide susceptibility. These findings emphasize the necessity of incorporating threshold and interaction effects in landslide susceptibility assessments, offering practical insights for disaster prevention and mitigation. Full article

Evaluating physical slope stability is essential to prevent landslides and damage to infrastructure located on sloping terrains. This study analyzes how static and pseudo-static conditions affect slope safety, considering the magnitude and location of the loads exerted. A total of 2394 simulations were carried out on 399 terrain profiles, using the Spencer method to calculate factors of safety (FSs). The results reveal that uniformly distributed loads placed at the center of the slope increase stability under static conditions. However, in pseudo-static scenarios, the action of dynamic forces, such as seismicity, drastically reduces the FS, especially on slopes greater than 15%. This analysis allowed the identification of critical zones of high susceptibility, promoting the implementation of reinforcement techniques, such as retaining walls and drainage systems. In addition, zoning maps were developed that prioritize safe areas for urban development, aligned with the international standards. The findings underscore the importance of integrating predictive models into design and planning processes, considering both static and dynamic factors. In conclusion, this study provides practical tools for risk mitigation and resilient infrastructure design in sloping terrains. Full article

In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting. Full article

Understanding the dynamic behavior of landslides is essential for effective risk assessment. This study examines the Yanguan landslide, which occurred on 29 October 2017, in the Three Gorges Reservoir (TGR) region of China. Due to its unique capability in modeling discontinuum behaviors during landslide fragmentation, the discrete element method was utilized to analyze the movement characteristics of this landslide. The investigation began with a field survey to assess the geological features and failure mechanism of the landslide, which indicates that the landslide was likely triggered by prolonged variations in reservoir water levels and heavy rainfall preceding the event. Following this, a three-dimensional numerical model of the landslide was constructed using pre- and post-event terrain data. The accuracy of the numerical model was validated by comparing its simulation results with field survey data. Finally, the landslide’s movement behavior and energy transformation were analyzed based on the validated model. This work can enhance landslide risk assessment by quantifying dynamic parameters critical for impact prediction, further provide a scientific basis for the study of the landslides in the TGR area, and contribute to disaster prevention. Full article

The impact generated by landslide-induced surge waves in large reservoirs poses significant threats to the safety of coastal residents and their property. It is essential to further elucidate the characteristics of these surge waves and enhance the capabilities of surge wave prediction and emergency warning systems. This research takes the Wangjiashan landslide in the Baihetan Hydropower Station reservoir area as a prototype, constructing a three-dimensional landslide model at a 1:150 scale. Through experiments designed under varying water levels and slope friction coefficients, the spatiotemporal evolution patterns of the landslide-induced surge waves along the riverbank were analyzed. The research results indicate that through the use of the zero-crossing method, fundamental characteristics of landslide-induced surge waves such as the maximum wave height, maximum period, significant wave height, and significant wave period could be obtained. Based on the statistical analysis of significant wave heights, the surge waves were categorized into three levels—small waves, moderate waves, and large waves—accounting for 15.79%, 78.95%, and 5.26% of the total waves, respectively. The height of surge waves decreases with an increase in the slope friction coefficient and river channel water depth. Additionally, the interaction between the landslide’s entry velocity into the water and the water level determines the effectiveness of wave propagation. This research provides crucial data support and theoretical foundations for the prediction and emergency warning of landslide-induced surge waves, offering significant implications for the prevention and mitigation of reservoir and landslide disasters. Full article

Field observations provide valuable information for rockfall assessments, but estimating physical and statistical quantities related to rockfall propagation directly is challenging. Simulations are commonly used to infer these quantities, but their subjectivity can result in varying hazard land use zonation extents for different projects. This paper focuses on the application of simulated trajectories for rockfall hazard assessments, with an emphasis on reducing subjectivity. A quantitative guiding rockfall hazard methodology based on earlier concepts is presented and put in the context of legislated requirements. It details how the temporal hazard component, related to the likelihood of failure, can be distributed spatially using simulated trajectories. The method can be applied with results from any process-based software and combined with various prediction methods of the temporal aspect, although this aspect is not the primary focus. Applied examples for static objects and moving objects, such as houses and vehicles, are shown to illustrate the important effect of the object size. For that purpose, the methodology was applied at an indicative level over Norway utilizing its 1 m detailed digital terrain model (DTM) acquired from airborne LiDAR. Potential rockfall sources were distributed in 3D where slopes are steeper than 50°, as most rockfall events in the national landslide database (NSDB) occurred in such areas. This threshold considerably shifts toward gentler slopes when repeating the analysis with coarser DTMs. Simulated trajectories were produced with an adapted version of the simulation model stnParabel. Comparing the number of trajectories reaching the road network to the numerous related registered rockfall events of the NSDB, an indicative averaged yearly frequency of released rock fragments of 1/25 per 10,000 m² of cliff was obtained for Norway. This average frequency can serve as a starting point for hazard assessments and should be adjusted to better match local conditions. Full article

The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions. Full article

Landslide dams, formed by natural disasters or human activities, pose significant challenges for lifespan prediction, which is crucial for effective water conservancy management and disaster prevention. This study proposes a hybrid CNN–Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. The model integrates CNN’s local feature extraction with Transformer’s global modeling capabilities, effectively capturing the nonlinear dynamics of key parameters affecting landslide dam lifespan. The IBKA ensures optimal parameter tuning, which enhances the model’s adaptability and generalization, especially when dealing with small-sample datasets. Experiments utilizing multi-source heterogeneous datasets compare the proposed model with traditional machine learning and deep-learning approaches, including LightGBM, MLP, SVR, CNN–Transformer, and BKA–CNN–Transformer. The results show that the IBKA–CNN–Transformer achieves R² values of 0.99 on training data and 0.98 on testing data, surpassing the baseline methods. Moreover, SHapley Additive exPlanations analysis quantifies the influence of critical features such as dam length, reservoir capacity, and upstream catchment area on lifespan prediction, improving model interpretability. This approach not only provides scientific insights for risk assessment and decision making in landslide dam management but also demonstrates the potential of deep learning and optimization algorithms in broader geological disaster management applications. Full article

The scanning curve of the soil–water characteristic curve (SWCC) represents the intermediate paths followed by soil as it transitions between the initial drying and main wetting cycles. The alternating occurrence of climatic conditions, such as rainfall and evaporation in different regions globally, provides a valuable framework for understanding how these dynamics influence the scanning curve. Monitoring the scanning curve can provide valuable insights for managing water resources and mitigating the impacts of drought, contributing to environmental sustainability by enabling more precise agricultural practices, promoting water conservation, and supporting the resilience of ecosystems in the face of climate change. It enhances sustainability by enabling data-driven designs that minimize resource use, reduce environmental impact, and increase the resilience of slopes to natural hazards like landslides and flooding. Available studies to determine the scanning curve of SWCC are limited and mostly conducted in the laboratory. This study aims to determine the real-time measurement of the scanning curve of SWCC for unsaturated soil. The research focuses on assessing the hysteresis behavior of residual soil slope from old alluvium through a combination of field instrumentation and laboratory testing. The pore size distribution was derived from the initial drying and main wetting SWCC. Field monitoring (scanning curve) indicates measurable deviations from the experimental results, including a 10% lower saturated water content and a 25% lower air-entry value. This study demonstrates the potential for field-based determination of scanning curves. It highlights their role in improving the prediction of the hydraulic behavior of residual slopes during varying climatic conditions. Full article

As one of the major forms of geological disaster, landslides cause huge casualties and economic losses in China every year. Given the importance of landslide prediction, it is a challenging task due to difficulties in efficiently leveraging the spatial–temporal information for enhanced prediction. This paper presents a novel spatial–temporal enhanced CNN-GRU model to improve landslide predictions with the following contributions. First, this paper explicitly models the spatial correlation in the dataset and constructs a spatial–temporal time-sequence deformation prediction model that greatly improves landslide predictions. This model integrates the spatial correlation of monitoring points into time-series deformation prediction to improve the prediction of landslide deformation trends. Second, we develop a complete data processing pipeline involving SBAS-InSAR, time-series data preprocessing, spatial–temporal homogeneous point selection and weighting, as well as CNN-GRU model training. The pipeline is tailor-designed to leverage the spatial–temporal correlation in the data to enhance the prediction performance. Third, we apply the proposed model to monitor landslide deformation around Woda Village, Chamdo City, Tibet. The results show that the root mean square error (RMSE) of the monitoring points in the landslide area is reduced by about 20.9% and the number of points with an RMSE of less than 3 mm is increased by 12.9%, leading to a significant improvement in prediction accuracy. Full article

Yu’nan County is located in the Pacific Rim geological disaster-prone area. Frequent landslides are an important cause of population, property, and infrastructure losses, which directly threaten the sustainable development of the regional social economy. Based on field survey data, this paper employs the coefficient of variation method (CV) and an improved TOPSIS model (Kullback-Leibler-Technique for Order Preference by Similarity to an Ideal Solution) to assess the social vulnerability to landslide disasters in 182 administrative villages of Yu’nan County. Also, it conducts a ranking and comprehensive analysis of their social vulnerability levels. Finally, the accuracy of the evaluation results is validated by applying the losses incurred from landslide disasters per unit area within the same year. The results indicate significant spatial variability in social vulnerability across Yu’nan County, with 68 out of 182 administrative villages exhibiting moderate vulnerability levels or higher. This suggests a high risk of widespread damage from potential disasters. Among these, Xincheng village has the highest social vulnerability score, while Chongtai village has the lowest, with a 0.979 difference in their vulnerabilities. By comparing the actual losses incurred per unit area from landslides, it is found that the social vulnerability results predicted by the CV-KL-TOPSIS model are more consistent with the actual survey results. Furthermore, among the ten sub-factors, population density, building value, and road value contribute most significantly to the overall weight with 0.269, 0.152, and 0.105, respectively, suggesting that in mountainous areas where the population is relatively concentrated, high social vulnerability to landslide hazards is a reflection of population characteristics and local economic level. The evaluation framework and evaluation indicators proposed in this paper can systematically and accurately evaluate the social vulnerability of landslide-prone areas, which provide a reference for urban planning and management in landslide-prone areas. Full article

In the field of slope landslide prevention and monitoring in open-pit mines, addressing the lag issues associated with the traditional GNSS inverse-velocity method, this study introduces a novel strategy that integrates high-spatiotemporal-resolution monitoring data from ArcSAR with a time log model for prediction. The key findings include the following: (1) This strategy utilizes the normal distribution characteristics of deformation velocities to set confidence intervals, accurately identifying the starting point of accelerated deformation. (2) Coupled with coordinate transformation, the time logarithm prediction method was constructed, unifying the units of measurement and resolving convergence issues in data fitting. (3) Empirical research conducted at the Kambove open-pit mine in the Democratic Republic of the Congo demonstrates that this method successfully predicts landslide times four hours in advance, with an error margin of only 0.18 h. This innovation offers robust technical support for slope landslide prevention and control in open-pit mines, enhancing safety standards and mitigating disaster losses. Full article

This study investigated the influence of non-landslide sampling strategies on landslide susceptibility assessment (LSA) performance and explored approaches to minimizing uncertainty in model selection. Five non-landslide sampling strategies were evaluated using the random forest (RF) model to generate landslide susceptibility maps (LSMs) for each scenario. To assess the impact of these strategies, this study employed a receiver operating characteristic (ROC) curve, a confusion matrix, and various statistical indicators. Additionally, the mean susceptibility indices derived from the gradient boosting decision tree (GBDT), support vector machine (SVM), and RF models were analyzed to evaluate their effectiveness in reducing the uncertainty during model selection. The GBDT, SVM, and RF were selected for their ability to handle complex, nonlinear relationships in the data, superior generalization capability, effective mitigation of overfitting risks, high predictive performance, and robustness. The findings revealed that selecting non-landslide samples from slope units without landslides enhances accuracy and averaging across models mitigated the uncertainty associated with landslide susceptibility models. Furthermore, this study demonstrated that the non-landslide sample selection method significantly improved prediction accuracy, particularly when samples were drawn from very-low-susceptibility zones identified by pre-classified machine learning models. These results highlight the importance of refining sample selection strategies and integrating multiple machine learning models to improve the reliability and accuracy of landslide susceptibility assessments. This approach provides valuable insights for future research and practical applications in risk mitigation and disaster management by offering a more precise depiction of low-susceptibility areas, thereby reducing the occurrence of false positives in landslide prediction. Full article

Monitoring and analyzing reservoir landslides are essential for predicting and mitigating geohazards, which are crucial for maintaining sustainability and supporting socio-economic development in reservoir areas. High spatiotemporal resolution is vital for effective reservoir landslide monitoring and analysis. For this purpose, we improved the resolution of the differential interferometric synthetic aperture radar (DInSAR) technique by fusing two-path deformation results from an overlapping Sentinel-1 area. First, we summarized the mathematical ratio relationship between deformation from the two paths. Second, time-series linear interpolation and time-reference difference removal were applied to the two separate deformation results of time-series DInSAR. Third, a ratio algorithm was adopted to fuse the deformation of the two paths into one integrated time-series result. The standard deviations of the deformation before and after fusion were similar, confirming the accuracy of the fusion results and feasibility of the method. From the integrated deformation, we analyzed the hydraulic impact, mechanisms, and physical processes associated with four reservoir landslides in the Three Gorges Reservoir area of China, accounting for rainfall and water-level data. The comprehensive analysis presented herein provides new insights on the hydraulic mechanisms of reservoir landslides and verifies the efficacy of this new integrated method for landslide investigation and monitoring. Full article

Accurate prediction of landslide displacement is crucial for disaster prevention and mitigation. This study proposes an STL-TCN-Transformer model that combines time series decomposition with deep learning to predict cumulative displacement. Using monitoring data from the Baishuihe landslide, the displacement sequence was decomposed into trend, periodic, and residual components using the STL method. The trend component, determined by geotechnical properties, was predicted using a univariate TCN-Transformer, while the periodic and residual components, influenced by rainfall and reservoir water levels, were analyzed for nonlinear correlations using the Spearman method and predicted with a multivariate TCN-Transformer. The proposed model achieved superior performance, with R² of 0.993, RMSE of 7.82 mm, and MAE of 5.82 mm, significantly outperforming EMD-LSTM, EEMD-RNN, and VMD-BiLSTM models in all metrics. These findings demonstrate the ability of the STL-TCN-Transformer to effectively capture the dynamics of landslide displacement, offering a reliable approach for landslide monitoring and early warning systems. Full article