Search Results (302)

Mountainous watersheds often suffer from incomplete and spatially biased landslide inventories, which limit the reliability of conventional susceptibility modelling. This study examines whether the erosion coefficient Z of the Gavrilović Erosion Potential Method can provide a process-oriented indicator of slope-instability predisposition in the Portaikos watershed, Central Greece. The Z coefficient was derived from geospatial layers representing vegetation protection, lithological erodibility, erosion-process expression and slope gradient, using Copernicus land-cover products, tree-cover density data, Sentinel-2 imagery, FABDEM and national soil–geological information. A landslide inventory of 46 mapped occurrences from the Hellenic Survey of Geology and Mineral Exploration was then used as an independent reference layer. Erosion severity was classified into five classes and compared with the landslide distribution through Frequency Ratio analysis. Most of the basin was assigned to moderate, very slight and slight erosion classes, covering 34.7%, 30.1% and 28.2% of the area, respectively. By contrast, severe and excessive erosion occupied only 6.7% and 0.4% of the watershed, but contained a much larger proportion of the mapped landslides: 58.7% and 10.9%, respectively. This disproportion was also reflected in the Frequency Ratio analysis. When the severe and excessive classes were considered together, they occupied approximately 7.1% of the watershed but contained 69.6% of the mapped landslides, corresponding to an FR value of 9.79. The separate excessive class showed the highest FR, but it was interpreted cautiously because of its very limited spatial extent and small landslide count. These results indicate that high Z values coincide with terrain sectors where lithological weakness, steep slopes, reduced surface protection and erosion-related sediment-source conditions jointly favour slope instability. The Gavrilović Z coefficient should therefore not be interpreted as a substitute for rainfall-threshold analysis or inventory-based predictive models. Rather, it may serve as a useful first-order screening layer for field verification, spatial prioritization and ecosystem-based mitigation planning in data-scarce Mediterranean mountain watersheds. Full article

Urban hillside areas concentrate hydrological and geotechnical risks intensified by accelerated urbanization and climate change. Although Nature-Based Solutions (NbS) are increasingly recognized as promising strategies for urban resilience, their application in hillside environments remains limited in scientific literature. This study integrates bibliometric analysis and a Systematic Literature Review (SLR) based on searches conducted across Scopus, ScienceDirect, and Web of Science from 2020 to 2025. Of the 4435 retrieved publications, only 92 addressed the association between NbS, hydrological modeling, and urban hillside environments. This reduction suggests that research integrating these themes remains limited within the adopted search criteria. The results demonstrate that hillside occupation in the Global South is conditioned by socio-spatial inequality, increasing exposure to landslides, erosion, and hydrological hazards. NbS were shown to reduce runoff peaks and contribute to slope stabilization when strategically positioned and adapted to slope gradient and hydrological connectivity; however, their effectiveness depends on continuous maintenance and monitoring. Comparative assessment indicates that most hydrological models are still applied in isolation, limiting the representation of coupled infiltration, soil saturation, and subsurface instability processes. The results indicate that effective NbS implementation in urban hillside areas requires integrated modeling approaches, interdisciplinary frameworks, and risk-oriented urban planning, particularly in socio-environmentally vulnerable contexts. Full article

This article examines social vulnerability (SV) as a necessary component of landslide risk assessment in the urban area of Quito, Ecuador. Landslide susceptibility identifies where slope instability is more likely, but it does not explain which populations have fewer resources to anticipate, cope with, or recover from such events. Using 2010 census-tract data, principal component analysis (PCA) was applied to derive interpretable factors of SV. The most robust factor—structural socioeconomic precariousness—combines precarious occupational conditions, lack of access to social security or private insurance, and limited access to new technologies. This factor was combined with a previously developed landslide susceptibility map (LSM) based on events recorded between 2005 and 2017 and aggregated to census tracts. The Comparative Environmental Risk Index (CERI) was then used to interpret whether socially vulnerable groups are disproportionately located in areas of higher landslide susceptibility. Results reveal a comparatively safer and socially advantaged populations axis from the center-north toward the eastern valleys, while high-risk and socially vulnerable areas concentrate in the south and selected peripheral zones. The study provides a historical and methodological baseline and contributes a quantitative, spatial, urban approach to landslide risk inequity in an Andean city. Full article

At approximately 05:51 on 22 January 2024, a catastrophic red-bed rock avalanche with a volume of 16 × 10⁴ m³ occurred in Zhenxiong County, Yunnan Province, southwestern China (105°00′47″ E, 27°28′54″ N). The detached rock mass buried 18 houses, killed 44 people, and caused economic losses of 145 million CNY (23.1 million USD). Field investigations, unmanned aerial vehicle (UAV) imagery, interferometric synthetic aperture radar (InSAR) analysis, and two-dimensional discrete-element modelling using PFC2D were conducted to examine the deformation characteristics, failure causes, and kinematics of the rock avalanche. The results show that internal factors, including high-relief terrain resulting from tectonic activity, fractured rock masses caused by joint cutting and weathering, and a soft–hard interbedded stratigraphic structure associated with argillaceous interlayers, were primarily responsible for the failure. Frost heaving may have been an important triggering factor for the Liangshuicun rock avalanche. Continued attention should be paid to slopes around the rock-avalanche site because ground deformation is still ongoing and may lead to future catastrophic events. The numerical simulation reveals that the kinematic process of the rock avalanche from initiation to final deposition lasted approximately 30 s. About 20 s after failure, the rock debris reached the village, burying houses and killing residents. The maximum velocity and displacement of the rock debris were 45 m/s and 335 m, respectively. These findings provide insight into the instability mechanisms and risk assessment of red-bed rock avalanches in the study area. Full article

Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning. Full article

Slope stability has consistently been a critical concern in mountainous road sections, with precipitation being the most significant factor precipitating slope instability. This study aims to elucidate the mechanism of slope instability under precipitation conditions and the extent of the impact of internal disaster-causing factors. To achieve this objective, a numerical simulation analysis method combining GeoStudio2018R2 and FLAC3D7.0 software was employed to conduct a comprehensive analysis of an unstable slope in Xinjiang. Regarding research methodology, cyclic precipitation and seasonal snowmelt were considered as external influencing factors. Initially, a two-dimensional model was constructed using GeoStudio software to analyze the spatial and temporal variations in pore water pressure and moisture content within the slope, elucidating their dynamic characteristics at different temporal and spatial scales. Subsequently, a three-dimensional numerical model was established using FLAC3D software to conduct a detailed analysis of the stress–strain state of the slope under various conditions, thereby obtaining disaster parameters such as displacement and sliding velocity in different directions. Through further comparison and verification of the overall stability analysis results of the slope obtained from both software packages, it was observed that they exhibited a consistent trend. The research findings indicate that under conditions of high-intensity short-term precipitation, the safety factor of the slope decreases to the lowest level, potentially leading to shallow landslides with smaller displacement but faster sliding velocity. Conversely, seasonal snowmelt and long-term localized precipitation have a more profound impact on the internal structure of the slope, with the sliding zone potentially penetrating into the deep bedrock. Although the occurrence frequency is low, the impact range is extensive. By combining two-dimensional and three-dimensional analyses, a comprehensive assessment of the different disaster-causing factors of the slope was conducted, enhancing the accuracy of the analysis results. The research findings provide a scientific basis and reference value for the formulation of subsequent slope protection and monitoring plans. Full article

Landslides are a significant natural hazard in regions with complex topographic, geological, and climatic conditions, where they can constrain sustainable territorial development and threaten infrastructure, land use, and environmental safety. This study aims to assess and map landslide susceptibility in Southern Primorye in order to support hazard-informed territorial planning and risk reduction. The analysis integrates vegetation, precipitation, geological, and topographic predictors with documented landslide occurrence data. A presence-only landslide susceptibility modeling approach was applied using the OneClassSVM algorithm with a radial basis function kernel. The results show that the highest susceptibility is associated with lower slope segments and coastal landforms composed of loose unconsolidated deposits and partly covered by sparse woodland. Surface runoff, subsurface flow, lithological conditions, and precipitation patterns were identified as the principal factors contributing to slope instability, while field observations confirmed that anthropogenic slope cutting related to road infrastructure may act as an additional local trigger. The model demonstrated moderate but acceptable predictive performance and allowed the delineation of areas with elevated landslide susceptibility. The resulting susceptibility map provides a regional-scale basis for more sustainable land-use planning, infrastructure placement, and landslide risk mitigation in Southern Primorye and in other regions with comparable environmental conditions. Full article

High-altitude rock slides frequently occur in the high-mountain canyon regions of the eastern Tibetan Plateau, posing significant disaster risks. The Baige landslide catastrophically failed in October 2018, blocking the Jinsha River and forming a major landslide-dammed lake. However, quantitative understanding of the spatiotemporal evolution and environmental control mechanisms remains insufficient, particularly regarding stage-dependent driving mechanisms. This study investigates the Baige landslide using mall Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), Seasonal-Trend decomposition based on Loess (STL) time-series decomposition, Principal Component Analysis–Independent Component Analysis (PCA-ICA) signal analysis, and slope-unit spatial statistics. Results indicate that: (1) deformation exhibited three stages separated by October 2018: slow pre-slide deformation, post-slide residual creep, and long-term sustained acceleration; (2) instability caused systematic restructuring of the deformation field, with valid pixels decreasing from 2766 to 560, deformation changing from slight positive line-of-sight (LOS) displacement to pronounced negative LOS displacement, and global standard deviation increasing from 21.40 mm to 40.55 mm, with stronger disturbances in the steep front zone; and (3) the driving mechanism shifted from short-term multi-factor control to a temperature-dominated long-term environmental control regime after failure, while gravity-driven creep and post-failure structural adjustment remained important background controls. Slope fragmentation and structural reorganization likely contributed to this transition. Full article

Landslides are recognised as one of the most widespread mass-wasting processes that modify the surface of Mars. Understanding the distribution of these processes is essential for identifying areas where slope failure conditions may occur and the factors that most strongly influence their occurrence. This study utilises a Frequency Ratio (FR) landslide susceptibility method to a landslide inventory in Valles Marineris, considering three landslide types. The analysis involves conditioning factors derived from topographic and structural data. The results underline the influential role of morphometric parameters in controlling landslide occurrence, with steep slope classes and high local relief values showing the strongest positive correlations with landslide distribution. The predictive performance of the susceptibility models is supported by Area Under the Curve (AUC) values of 0.82 for Slumps, 0.78 for Rock Avalanches, and 0.75 for Debris Flows, indicating good model reliability. Proximity to tectonic structures appears to contribute to landslide occurrence, suggesting that structurally weakened rock masses or past seismic activity may influence slope instability in the region. Overall, the results display the potential of statistical landslide susceptibility approaches for analysing slope instability processes in planetary environments and provide a new toolkit for future investigations on Mars. Full article

Rainfall-induced delayed instability of fractured rock slopes is strongly affected by fracture preferential flow, hydro-mechanical coupling, and spatial matrix heterogeneity. However, the coupled influence of stress-dependent fracture aperture evolution and heterogeneous matrix properties on delayed slope deformation remains insufficiently quantified. In this study, a two-dimensional discrete fracture network (DFN)–equivalent continuum coupled model was established using spectral random field theory and a representative Monte Carlo-generated fracture geometry. The spectral exponent β = 1.0–2.5 was adopted to characterize different degrees of matrix heterogeneity, and rainfall infiltration–stress coupling simulations were conducted under an extreme rainfall scenario followed by drainage. The results indicate that the wetting front advances irregularly in the heterogeneous matrix, while fracture preferential flow accelerates rainwater infiltration and promotes local pore-pressure accumulation near the phreatic surface. After rainfall cessation, water stored in fractures continues to recharge the deep matrix, leading to delayed pore-pressure increase and post-rainfall deformation. The simulated fracture aperture shows an initial closure followed by gradual dilation, which is controlled by the competition between saturation-induced stress redistribution and pore-pressure-driven effective stress reduction. Under a common strength reduction factor of FOS = 1.4, stronger matrix heterogeneity results in more pronounced plastic strain concentration and larger displacement amplitude along the potential slip zone. These findings suggest that fracture aperture evolution and matrix heterogeneity jointly influence delayed deformation and potential failure-zone development in rainfall-affected fractured rock slopes. The conclusions should be interpreted within the scope of a two-dimensional DFN–equivalent continuum numerical framework with prescribed rainfall conditions and representative fracture/random-field realizations. Full article

Red clay in humid-hot environments suffers from severe water sensitivity and rainfall-induced slope instability, while traditional cement/lime stabilization faces high carbon emission challenges. Existing studies on plant ash-modified red clay mainly focus on basic mechanical properties, while systematic research on water retention characteristics and slope stability under extreme rainfall in humid-hot climates remains insufficient. To address this gap, this study proposes a sustainable stabilization method using agricultural waste-derived plant ash for red clay modification in humid-hot regions. Red clay exhibits distinct engineering behaviors owing to its unique physicochemical properties, leading to compromised slope stability and reduced resistance to rainwater infiltration. In this study, red clay was stabilized with 5%, 10%, 15%, and 20% plant ash. Laboratory tests evaluated compaction characteristics, shear strength, and water retention, supported by microstructural analysis via scanning electron microscopy (SEM). Slope stability under rainfall conditions was further simulated using ABAQUS 2022 software. Key findings include: (1) The addition of plant ash significantly altered the compaction properties. As the plant ash content increased from 0% to 20%, the maximum dry density of the modified red clay decreased linearly from 1.68 g/cm³ (unmodified soil) to 1.53 g/cm³, while the optimum moisture content rose from 21.86% to 23.85%. (2) The mechanical properties exhibited a non-linear response, peaking at 10% ash content. At this optimum dosage, the unconfined compressive strength, cohesion, and internal friction angle increased by 70.4%, 83.0%, and 37.1%, respectively, compared to untreated soil. (3) Plant ash enhanced water retention capacity, shifting the soil-water characteristic curve (SWCC). The modified soil demonstrated faster dehydration at low suction but improved water retention at high suction. The permeability coefficient decreased by an order of magnitude. Microstructural analysis revealed reduced porosity and fracture infilling by cementitious gels. (4) Numerical simulations confirmed that 10% plant ash reduced maximum slope displacement from 0.96 m to 0.61 m under heavy rainfall (90 mm total precipitation over 36 h, peak intensity 90 mm/day), elevating the safety factor from 0.85 to 1.45. Failure modes transitioned from deep-seated slip to localized shallow erosion. These results demonstrate that plant ash is a sustainable and effective additive for red clay slope stabilization in tropical climates. Full article

Rainfall-induced landslides are destructive natural hazards that require timely detection and early warning to protect lives and infrastructure. This study presents the development and deployment of an IoT-based, cost-effective, real-time monitoring and early warning system that integrates surface and subsurface sensors to detect slope instability and issue timely warnings for disaster prevention. The monitoring system integrates tilt sensors, volumetric water content sensors, a MEMS-based inclinometer, a rain gauge, and a video camera, all linked to a web-based platform. Field results demonstrated that the tilt sensors effectively detected surface displacement, the volumetric water content sensors responded rapidly to rainfall infiltration, and the MEMS-based inclinometer captured subsurface displacement during rainfall events. Detailed analysis was conducted using multisource monitoring datasets collected during three specific rainfall events. An early warning method for landslides was proposed by combining the tilt rate, horizontal displacement rate derived from the MEMS-based inclinometer, and saturation index. Accordingly, critical threshold values for different warning levels were established based on tilt rate (T_r), displacement rate (D_r), and saturation index (S_i). This study provides a robust strategy and guidelines for early warning systems, enabling generation of warning alarms and demonstrating immense potential to reduce the impacts of rainfall-induced shallow landslides and enhance risk management. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article

The Ili River Valley is a typical seasonally frozen region in which slope instability frequently occurs during the warm frozen-soil stage, generally at temperatures ranging from approximately −1.5 to 0 °C. In this context, changes in unfrozen water content play an important role in controlling the pore structure and mechanical behavior of warm frozen soil, yet the links among these factors remain insufficiently understood. This study investigates warm frozen soil from the Ili River Valley, with particular emphasis on the role of unfrozen water content in regulating pore-structure characteristics and mechanical response under low-temperature conditions. Low-field nuclear magnetic resonance (NMR), low-temperature triaxial shear tests, scanning electron microscopy (SEM), and quantitative image analysis were employed to examine the relationships between unfrozen water content, pore structure, and macroscopic mechanical properties under different temperatures, initial water contents, and confining pressures. The results show that unfrozen water content decreases markedly with decreasing temperature, especially within the range of −1.5 to −5 °C, and increases with increasing initial water content. These changes are accompanied by significant variations in porosity, pore abundance, and pore fractal dimension, reflecting freezing-induced reorganization of the pore system. Lower temperatures and higher initial water contents promote ice-crystal growth and the formation of larger ice-cemented aggregates, thereby modifying the pore framework. Meanwhile, peak strength and cohesion increase with decreasing temperature and increasing initial water content, whereas the internal friction angle shows a decreasing trend. In addition, porosity, pore abundance, and pore fractal dimension are closely correlated with peak strength and cohesion. The results indicate that unfrozen water content governs the freezing-induced reorganization of pore structure, which in turn controls the strength evolution of warm frozen soil. These findings improve understanding of the role of unfrozen water in low-temperature soil structure and strength evolution and provide a basis for evaluating slope instability in the Ili River Valley. 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