Search Results (1,052)

Landslides are a recurrent geohazard in Andean urban environments, where weak soils, intense seasonal rainfall, and unplanned urban expansion combine to increase slope vulnerability. In such settings, sustainable hillside management requires stabilization strategies that are both technically effective and environmentally compatible. This study evaluates the effect of root reinforcement by vetiver grass (Chrysopogon zizanioides) on slope stability in two representative soils from Loja, Ecuador: sandy silt (SM) and sandy clay (SC). A reduced-scale physical model with 30 days of root development was established, and consolidated–drained direct shear tests (ASTM D3080/D3080M-23) were performed to determine the shear strength parameters under bare and vetiver-reinforced conditions. These parameters were then incorporated into numerical slope stability analyses using Slide and PLAXIS 2D, considering three slope angles (30°, 45°, and 50°), six root-positioning configurations, and hydraulic conditions with and without a water table. Vetiver increased effective cohesion by 22.7% in sandy silt and 19.0% in sandy clay, while the internal friction angle increased by 21.8% and 12.2%, respectively. Across all modeled scenarios, vetiver produced a consistent improvement in the factor of safety. The most critical case, corresponding to sandy silt at 45° with a water table, increased from FS = 0.841 in the control condition to FS = 1.309 under the full-coverage configuration. Parametric sensitivity analysis yielded coefficients of variation between 4.97% and 7.03%, indicating a stable model response under controlled parameter perturbations. These findings support vetiver as an experimentally grounded and environmentally sustainable Nature-based Solution for slope stabilization and provide relevant evidence for sustainable management of hazard-prone urban hillsides in vulnerable Andean settings. Full article

Bench slopes in open-pit mines are highly susceptible to progressive deformation and instability due to the coupled effects of excavation disturbance, rock mass weathering, and extreme rainfall, posing significant challenges to rapid risk assessment and engineering decision-making. To address the limitations of conventional methods in efficiency and adaptability under complex multi-factor conditions, this study proposes a hybrid simulation–artificial intelligence framework for rapid slope stability assessment and bench face angle optimization. Multi-scenario numerical simulations were conducted by integrating geological investigation data, laboratory and in situ mechanical parameters, and extreme rainfall conditions to characterize slope deformation and failure mechanisms and generate a dataset for machine learning model training. Machine learning models were trained using slope height, bench face angle, unit weight, cohesion, and friction angle as inputs, and safety factors under natural and extreme rainfall conditions as outputs, with hyperparameters optimized by Bayesian optimization. The results indicate that highly weathered rock masses dominate shallow deformation and act as critical weak zones, while extreme rainfall significantly accelerates instability evolution and reduces slope safety factors. Among the RF, SVR, and ELM models, the Bayesian-optimized support vector regression (BO-SVR) exhibits the best predictive performance (R² > 0.98). SHapley Additive exPlanations (SHAP) analysis reveals that slope height and shear strength parameters are the dominant controlling factors, whereas unit weight has a relatively limited influence. Validation using real landslide cases shows good agreement with numerical simulations, confirming the reliability of the proposed framework. The developed approach enables rapid risk evaluation and supports bench face angle optimization, providing an effective tool for intelligent slope management in open-pit mining. Full article

Although deep learning has been successfully used to detect landslide hazards in recent years, existing methods still face challenges due to the variety of landslide characteristics in different terrains and topographies. This study proposes a new framework for landslide detection by comparing various YOLO models. It employs deformable convolutional modules combined with GhostConv modules to enhance feature extraction for landslide targets. The framework uses a structured IoU loss function to optimize the alignment of actual and predicted frames in a directional sense. Additionally, it introduces the CoordAtt attention mechanism to accelerate model convergence and improve training efficiency. The experimental results demonstrate that the enhanced YOLO model (CDCS-YOLO), incorporating four key enhancement modules (Coordinate Attention, Deformable Convolutional Networks, the C3 Module/CSP Architecture and SIoU Loss), achieved a maximum mAP of 96.6%, an accuracy of 96.1%, and a frame rate of 142.6 FPS. Notably, it performed exceptionally well in soil landslide detection, achieving an average detection accuracy surpassing 90%. Based on the experimental results, we explored a morphological landslide classification method further as well as a multi-source differential monitoring strategy integrating UAV imagery, field surveys, ground-based LiDAR data, rainfall information and deformation indicators. The proposed method outperforms the baseline approach and is a promising solution for detecting landslides and geological hazards in Xinjiang. Full article

The spatial representativeness of rain gauges is critical for accurately estimating rainfall that triggers landslides and for defining operational thresholds. This study evaluates the potential error in conventional rain-gauge-based methods for estimating landslide-triggering rainfall, using 548 landslide events across Italy from the e-ITALICA database, which reports the duration of each rainfall event and the location of the nearest available rain gauge. A radar-based assessment, using the Surface Rainfall Intensity (SRI) product (1 km² resolution) provided by the Italian Department of Civil Protection, quantified discrepancies between rainfall at landslide locations and at the nearest rain gauges. Seasonal analysis was performed, considering summer events (April–September), typically associated with convective and spatially variable rainfall, and winter events (October–March), generally more stratiform and uniform rainfall. Results indicate that the probability of large discrepancies increases with distance. Summer events show larger discrepancies at short distances compared to winter events, but seasonal distributions converge at larger distances. These findings provide useful insights into rain gauge representativeness in studies of rainfall-induced landslides. Full article

Given the escalating impacts of global climate change and extreme weather events, the accurate stability assessment of rainfall-induced landslides necessitates a comprehensive consideration of both seepage processes and the inherent spatial variability of soils. Traditional deterministic and two-dimensional (2D) analyses often fail to capture the multi-dimensional kinematic features of slope failures and the stochastic nature of soil heterogeneity, thereby leading to inaccurate risk assessments. This study proposes a three-dimensional (3D) slope reliability analysis framework. Within this framework, a 3D slope geometric model is constructed using GeoStudio 2025.1.0 software, and seepage analysis is conducted by the SEEP3D module. To account for soil spatial variability, the Karhunen–Loève (K-L) expansion method is employed to discretize key shear strength parameters (effective cohesion and effective angle of internal friction). The factor of safety (F_s) is evaluated using the 3D simplified Bishop method, which is then coupled with Monte Carlo simulations to determine the probability of failure (P_f). The results show that rainfall infiltration causes progressive dissipation of shallow matric suction and a significant rise in the groundwater table near the slope toe, resulting in reduced effective stress in the critical resistance zone. As rainfall intensity increases, the F_s decreases approximately linearly from 1.14 to 0.90, whereas the P_f increases nonlinearly from nearly 0 to 98.36%. Under the rainstorm condition, although the F_s remains above unity at 1.063, the corresponding P_f reaches 23%, indicating that deterministic evaluation based only on the F_s may underestimate the actual failure risk. The proposed framework provides a quantitative tool for evaluating rainfall-induced slope instability by integrating transient hydraulic response, three-dimensional spatial variability, and probabilistic reliability assessment. Full article

Forecasting short-term heavy precipitation is crucial for the early warning of disasters such as flash floods, landslides, and urban flooding. However, under complex topographic conditions, traditional numerical forecasts still fall short in capturing high-resolution heavy precipitation events, and conventional radar extrapolation methods struggle to accurately characterize the nonlinear evolution of weather systems during advection, deformation, and intensity adjustment processes. To address the challenge of short-term heavy rainfall forecasting in high-altitude, complex terrain, this paper proposes Nowcast with Flow-Net (Nwf-Net), a short-term precipitation forecasting framework that integrates deep learning with multi-source meteorological data. This framework consists of a Morphological Evolution Track Module (MET) and a Rainfall Intensity Correction Module (RIC) connected in series: the former combines upper-air wind fields with traditional optical flow algorithms to jointly characterize the displacement of and morphological changes in radar echoes; the latter utilizes a deep recurrent neural network to correct the intensity of forecast results, thereby enhancing the model’s ability to characterize the evolution of strong convective echoes. Experiments in the Alpine region demonstrate that Nwf-Net achieves CSI, HSS, and F1 scores of 0.392, 0.506, and 0.546, respectively, at 32 dBz. These results outperform those of traditional numerical models and some mainstream models, indicating that Nwf-Net can accurately capture multiscale severe convective information and consistently generate precise forecasts. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas. Full article

In a high-mountain gorge region, landslide hazards pose a serious threat to the upper Minjiang River, located at the eastern edge of the Tibetan Plateau. To map susceptibility in the upper Minjiang River basin, this study used a Random Forest model in conjunction with slope unit subdivisions. First, a landslide inventory containing 3785 landslides was established using human–machine interactive interpretation techniques. After a multicollinearity analysis, 11 key conditioning factors were selected to construct a spatial database, including elevation, slope, aspect, curvature, topographic wetness index, stream power index, distance to fault, peak ground acceleration, distance to road, vegetation index, and rainfall. The r.slopeunits algorithm was implemented to partition the study area into discrete slope units. The ideal parameter combination for slope units was determined through integrating the normalized slope aspect standard deviation and Moran’s I using an equal-weight scheme. Ultimately, 30,513 slope units were delineated in the upper Minjiang River. The random forest model trained on these ideal slope units was validated using a 70/30 split of landslide and non-landslide samples. In receiver operating characteristic (ROC) curve analysis, the model demonstrated excellent performance, with an area under the curve (AUC) of 0.852. The results indicate that small-scale landslides dominate the inventory in terms of frequency. Despite accounting for only 30% of the study area, the Very High and High susceptibility zones exhibit considerable degree of spatial overlap with current landslide clusters. Furthermore, shapley additive explanations (SHAP) explanatory metrics indicate that the random forest model’s predictive behavior is primarily influenced by terrain elevation, precipitation patterns, and proximity to transportation networks. Full article

The power system is the backbone of the energy network, and overhead lines are its vital structures. Weather threats may jeopardise the reliability of lines and make them a weak link. In particular, heavy rainfall episodes can cause failures, especially in mountain areas. Current climate changes may exacerbate the effects on the ground, intensifying rainfall episodes and increasing the frequency of extreme events. In this context, debris flows triggered by rather intense precipitation and characterised by fast kinematics can destroy pylons and electric connections, affecting the infrastructures not only in the upper ridges but also downstream across the fan apex, where powerlines are much more distributed. This study presents an in-depth back-analysis of two debris flow events triggered in concomitance with a heavy cloudburst that occurred in Talamona (Sondrio Province, Italy) in July 2008 and in Campo Tartano (Sondrio Province, Italy) in April 2024. These events hit onsite powerlines, causing blackouts and showing the potential vulnerabilities of the local electricity system. An analysis of rainfall-induced landslide failure is carried out using the numerical model CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) and MIST-DF (Modelling Impulsive Sediment Transport—Debris Flow) with the aim of reconstructing the dynamics of the first (i.e., Talamona) geo-hydrological event. Powerline vulnerability is also investigated against debris flow dynamics, discussing possible strategies to reduce pylon exposure and to increase the resilience of the local electro-energetic network. Since, under climate change scenarios, heavy rainfall episodes are projected to intensify, an alternative approach based on rainfall-threshold curves is presented and applied to both cases of study. The latter, already implemented for civil protection purposes, could be useful in early-warning procedures against potential debris flow hazards. For both methodologies, the findings from the study confirm the strength of the approaches and foster their application in different situations (back-analysis and early warning) to reduce powerlines’ geo-hydrological risks. Full article

Rainfall-induced landslide early warning systems require reliable estimation of soil moisture conditions. This study proposes a Soil Water Index (SWI) framework based on a three-stage TANK model. Through GLUE (Generalized Likelihood Uncertainty Estimation)-based behavioral parameter sampling and K-means clustering, SWI response characteristics were classified into two representative scenarios: slow drainage (Scenario 1) and fast drainage (Scenario 2). Two-stage thresholds—Watch (α = 0.40 × SWI_peak) and Warning (β = 0.70 × SWI_peak)—were established from SWI rise profile analysis at 500 m and 5 km resolutions, providing 20–27 and 4–5 h of lead time, respectively. Verification against the July 2025 heavy rainfall event across multiple resolutions and spatial extents yielded Hit Rates of 0.984–1.000, while FAR (False Alarm Ratio) remained structurally high (0.607–0.648 for grids sharing the rainfall field with occurrence sites). These findings confirm that SWI serves as an effective regional-scale necessary condition indicator for landslide-triggering moisture, but FAR reduction requires integration with slope susceptibility information. Full article

The Dongchuan District in Kunming City is located in the transition zone between the Yunnan–Guizhou Plateau and the Sichuan Basin. As a region with a copper mining history of over 2000 years, the district has experienced frequent landslides that pose serious threats to human lives, property, and ecological sustainability. Therefore, it is essential to compile a comprehensive landslide inventory and analyze the relationships between landslide spatial distribution and influencing factors for geological hazard prevention. High-resolution remote sensing imagery was interpreted to establish a landslide inventory, based on which the spatial distribution and geometric characteristics of landslides were systematically analyzed. The results show that a total of 1623 landslides were identified, with a total area of 10.36 km². Landslides predominantly occur at elevations of 1000–2000 m, on slopes of 20–45°, with aspects of 255–285°, and relief between 150 and 400 m, in areas with annual rainfall below 825 mm, within 1000 m of rivers and 3000 m of fault lines, and 1000–5000 m of mines. Four landslide clusters were delineated along the Xiao River Fault, highlighting the significant influence of the fault on the spatial distribution of landslides. Most landslides are longitudinal in planform, with travel distances (L) of 50–450 m and heights (H) from 25 to 350 m, both exhibiting allometric scaling with volume. The mean H/L ratio is 0.56 (corresponding to a mean reach angle of 29°), significantly higher than that in Baoshan City (21°). The results provide insights into landslide initiation mechanisms and spatial distribution patterns on the northern margin of the Yunnan–Guizhou Plateau, offering valuable data for landslide hazard assessment and sustainable regional development. Full article

Geological hazards are characterized by their sudden occurrence, high destructiveness, and wide spatial impact. In particular, landslides and debris flows triggered by earthquakes and intense rainfall often lead to severe casualties and substantial property losses. Therefore, the rapid delineation of affected areas is crucial for disaster assessment and post-disaster reconstruction. To this end, several geohazard datasets have been developed from remote sensing imagery, focusing on specific regions, disaster types, and data sources, providing valuable support for geohazard detection and risk assessment. Our study addresses the diversity of real-world geological disasters in terms of their types, causes, and spatial distribution and constructs the Composite Geological Hazards Dataset (CGHD), a dual-temporal geohazard dataset that enhances generalisation and practical applicability. CGHD incorporates pre- and post-disaster remote sensing images of 14 landslide and debris flow events that occurred worldwide between 2017 and 2024, collected using four remote sensing platforms and encompassing multiple spatial scales and land-cover categories. The affected areas varied significantly in size and shape, with land-cover types including roads, buildings, vegetation, farmland, and water bodies. This resulted in 3963 pairs of pre- and post-disaster images, each with a size of 1024 × 1024 pixels. We validated the reliability of the CGHD through experiments with nine change-detection models and further evaluated its generalisation capability using an unseen dataset. The experimental results demonstrate that CGHD achieves high recognition accuracy and strong generalisation across diverse geographic environments, providing comprehensive data support for intelligent geohazard recognition and disaster assessment. Full article

Sediment connectivity is a key indicator of whether eroded sediment can be efficiently transported within a catchment. Landslides are a major form of rainfall-induced erosion on the steep slopes of the Loess Plateau and contribute substantially to overall catchment sediment yield. However, evaluating the connectivity of landslide-derived sediment and its implications for sediment transport risk remains challenging. Therefore, field investigations were conducted in three watersheds (R1, R2, and R3) on the Loess Plateau to examine landslides triggered by rainstorms. We analyzed the characteristics of landslide erosion and its influencing factors, applied graph theory to investigate sediment connectivity after landslides occurred, and assessed the risk of sediment transport to the catchment outlet. The results showed that the landslide number densities in the catchments R1, R2, and R3 were 9, 155, and 214 km⁻², respectively. The average erosion intensities were 25,153, 53,074, and 172,153 t km⁻², respectively. The network analyses indicated that the locations of landslides within the catchments were primarily concentrated in areas with high transport networks and high sediment accessibility to the catchment outlets. The sediment connectivity index further showed that 59%, 43%, and 51% of landslides in the three watersheds, respectively, were at high risk of delivering sediment to the catchment outlet. Accordingly, measures such as slope drainage and gully dam construction may help reduce both landslide occurrence and sediment transport. These findings provide new insights into the transport risk of eroded sediment from a connectivity perspective, identify hotspot areas of sediment connectivity and landslide erosion, and support the targeted prevention and control of catchment erosion. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article