Search Results (615)

This study presents DEDICA (Database of Hydrogeological Instability Events in Calabria, southern Italy), developed by the District Basin Authority of the Southern Apennines (ABDAM) in collaboration with the CNR-IRPI. The database integrates digitized historical sources, chronicle-based records, and previously unpublished archival data that were systematically analyzed, validated, and georeferenced within a GIS environment. After two years of development, DEDICA includes 5329 landslides, 2097 flood events, and 1711 urban flooding occurrences spanning the period 1900–2025. The system supports continuous data updating, enabling both the integration of recent events and the refinement of historical records. The database provides a comprehensive tool for identifying areas prone to geo-hydrological hazards based on historical recurrence, supporting hazard assessment, land-use planning, and risk management strategies. The methodological framework, database structure, and data processing workflow are described in detail. Spatio-temporal analyses highlight the distribution of instability processes, identifying the most affected sectors and revealing seasonal patterns and long-term trends. DEDICA represents a pilot initiative within a broader program aimed at extending the inventory to all regions under ABDAM jurisdiction, ultimately contributing to the development of a unified geo-hydrological hazard database for southern Italy. Full article

Large deformation runout is a key factor in assessing the hazards posed by underwater landslides. However, conventional kinematic analyses often neglect both the progressive degradation of slope materials and the hydrodynamic response accompanying the interaction between the moving mass and the overlying water. Taking the well-documented St. Niklausen underwater landslide as a representative case, this study employs a coupled Eulerian–Lagrangian (CEL) model to investigate the earthquake-triggered initiation, large deformation movement, and hydrodynamic response of the landslide. A Python 2.7.15-based stress mapping method is developed to establish an accurate initial geostatic stress field for the irregular slope profile. The numerical model reproduces the principal stages of landslide initiation, runout, and deposition. The results reveal a progressive retrogressive failure mechanism in which successive sliding masses interact through a high-strength compression zone. The rear sliding mass continuously transfers compressive work to the frontal mass, thereby maintaining its downslope movement and indirectly promoting basal erosion to a maximum depth of approximately 6.2 m. In addition, rapid landslide motion generates pronounced vortical flow in the overlying water. These flow structures reflect the hydrodynamic response induced by landslide motion, although their net influence on basal resistance and final runout cannot be isolated from the present coupled simulation. These findings clarify the internal mechanical evolution of underwater landslide movement and characterize the accompanying hydrodynamic response, providing a methodological basis for assessing landslide mobility and related underwater hazards. Full article

Earthquake-induced accidents involving buried oil and gas pipelines in water-network regions are governed by coupled seismic, hydrological, geotechnical, and emergency-response factors, while complete accident records are scarce. To support scenario-based consequence analysis under sparse-data conditions, this study develops an accident scenario analysis framework that integrates numerical simulation with Bayesian probabilistic inference. Scenario elements are organized according to four categories: disaster-causing factors, elements at risk, hazard-inducing environment, and emergency management. Finite element analysis and computational fluid dynamics are used to quantify pipeline mechanical response and hydraulic-scour effects, and the resulting physical responses are embedded in a dynamic Bayesian network as state evidence and transition constraints. Triangular fuzzy numbers are used to process expert evaluations and determine node probabilities. The resulting multi-mechanism simulation-Bayesian inference framework quantifies the accident chain from earthquake loading to pipeline deformation, leakage, fire or explosion, and emergency control. Forward reasoning estimates the probability of each scenario state, sensitivity analysis identifies key drivers, including strong earthquakes triggering landslides and rainfall during flood seasons, and disaster-chain analysis clarifies the dominant causative pathways. The framework provides a reproducible basis for scenario analysis, consequence assessment, monitoring and early warning, and emergency response planning for buried oil and gas pipelines exposed to seismic hazards in water-network regions. Full article

Sustainable landslide hazard mitigation requires scenario-based assessment of nature-based solutions under realistic hydromechanical and multi-hazard conditions. This study evaluates the mechanical effect of Vetiver grass (Chrysopogon zizanioides) on slope stability in Loja, southern Ecuador, through an integrated framework combining geotechnical characterization, direct shear testing, finite element modelling, limit equilibrium analysis, and targeted statistical evaluation. Three fine-grained soils, classified as CH, MH, and ML, were analysed under baseline groundwater conditions, groundwater with an 8 kN/m² surcharge, and groundwater with surcharge plus pseudo-static seismic loading. Vetiver reinforcement increased apparent cohesion by 8.92–27.65% and internal friction angle by 6.90–17.43%, with the highest cohesion gain in ML soil. Numerical results showed that stabilization was controlled by soil type, slope geometry, loading condition, and interaction between the 2.0 m root-reinforced layer and the governing failure mechanism. Under surcharge loading, FS for ML at 0.5H:1V increased from 1.056 to 1.450. Under combined loading, FS increased from 0.217 to 1.440 for ML at 1H:1V and from 0.587 to 2.060 for CH at 1H:1V. Targeted ANOVA/MANOVA for MH soil confirmed the influence of geometry and combined loading. Therefore, Vetiver should be considered a complementary, site-specific, and risk-informed mitigation measure rather than a universal stabilization solution. Full article

As a natural event that poses a serious threat to human life, property, and the natural ecology, the effective identification, assessment, and early prevention of geological hazards are crucial. Hunan Province in China is a region with a high incidence of geological hazards, exhibiting complex chain-generated characteristics due to the influence of terraced topography, heavy rainfall, and human activities. Existing landslide monitoring methods have insufficient ability to capture weak deformation at small spatial scales, making it challenging to identify landslide disaster precursors in this region effectively. This paper proposes a multi-resolution SAR collaborative monitoring method using SBAS-InSAR technology for wide-area screening, followed by a joint PS/DS-InSAR processing framework to identify weak deformation signals at small spatial scales. Using 2441 registered geohazard sites in the work area as the background dataset, wide-area InSAR monitoring and remote-sensing interpretation delineated 180 suspected geohazard target areas. Field investigation confirmed 83 of the 180 candidate target zones as active hidden-danger points, corresponding to a field-confirmed rate of 46.11% among the interpreted candidates. 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

Landslide susceptibility assessment acts as a core technical tool for geological disaster governance, ecological protection and long-term risk mitigation strategies. This modeling approach quantifies the possibility of slope-collapse events and delivers objective decision-making support for regional geologic environment supervision. To overcome the low computational efficiency and weak capacity of conventional evaluation frameworks to extract multi-level spatial grid rules, this paper takes Nanning City, the capital and largest city of the Guangxi Zhuang Autonomous Region in southern China, as the research object. Ten types of terrain and geological control factors combined with historical landslide inventory records are adopted to build a two-stage coupled evaluation framework integrating the information value method (IVM), a Bidirectional Temporal Convolutional Network (BiTCN) and Transformer, named IVM-BiTCN–Transformer. The hierarchical framework first adopts IVM to finish preliminary hazard grading and calculate factor contribution weights, then inputs classified grid samples into the BiTCN-Transformer module to realize local terrain feature and global factor fusion, which significantly lifts the overall identification precision. Ten widely adopted landslide evaluation algorithms are selected for contrast simulation, with multiple quantitative metrics adopted to judge model reliability. Experimental outcomes prove that the presented IVM-BiTCN–Transformer framework obtains superior hazard discrimination capacity, which can raise the precision and stability of landslide zoning and offer reliable technical support for targeted regional geological disaster prevention. Full article

Landslides pose a significant natural hazard requiring cost-effective, sustainable mitigation strategies. Nature-based solutions utilizing vetiver (Chrysopogon zizanioides) demonstrate considerable potential due to its deep, dense root system, which enhances soil reinforcement. This study examines the effects of growth medium composition and NPK fertilization on the root mechanical properties, biomass accumulation, and vegetative growth of vetiver under controlled greenhouse conditions. The planting medium comprised landslide-affected soil mixed with compost in varying proportions, combined with different NPK fertilizer levels. Root tensile strength was measured to assess mechanical performance, while biomass and vegetative parameters were evaluated to determine growth responses. Results indicate that both growth medium composition and fertilization significantly influence biomass production, root diameter, and tensile strength. The highest biomass (178.33 g) was recorded under T80P20N7.5, while the greatest root diameter (1.47 mm) and tensile strength (21.9 MPa) were observed under T100P0N0. These outcomes suggest a trade-off between biomass production and mechanical reinforcement. Overall, optimizing soil amendments and nutrient inputs enhances vetiver’s bio-reinforcement capacity, supporting its application in sustainable slope stabilization and landslide mitigation. Full article

Recent evidence shows that multi-hazard events are becoming more frequent across Europe, highlighting the need to move beyond single-hazard approaches and toward integrated risk assessment. Despite recent advances, four key gaps persist: limited quantitative research on hazard interactions; model complexity that restricts large-scale applicability; narrow hazard coverage with insufficient integration of climate change scenarios; and neglect of cumulative impacts from sequential events. This study makes two complementary contributions. First, it proposes a scalable, unified multi-hazard risk assessment framework applicable at regional and European scales. In this framework, multi-hazard considerations are embedded throughout the entire assessment process—from study domain definition and loss metrics, through hazard characterization and conceptual incorporation of dynamic vulnerability, to the probabilistic treatment of hazard interactions and compound effects via a probabilistic, conditional-dependency framework conceptually represented as a Bayesian network. Second, based on the literature review conducted in this study, no prior European study was identified that combines flood, earthquake, and earthquake-triggered landslide hazards at the asset level for educational facilities. Therefore, this work is, to the best of the authors’ knowledge, among the first such quantitative, asset-level multi-hazard risk assessments. The framework is demonstrated for over 1700 school buildings in the Region of Central Macedonia, Greece, using pan-European open-access datasets (ESHM20, ESRM20, JRC, GIRI, and ELSUS v2), making it readily transferable across Europe. By supporting risk-informed prioritization of mitigation and resilience investments, this work is consistent with the broader objectives of the Sendai Framework and the UN Sustainable Development Goals, particularly SDG 11 and SDG 13. Full article

Landslides are among the most common and destructive geological hazards and pose a significant threat to the long-term stability of infrastructure systems. In particular, long-distance power transmission corridors often traverse mountainous and forested regions, where landslides can endanger tower foundations and transmission line safety. Such landslides predominantly occur in sloped forested areas, where dense vegetation causes severe occlusion that blurs landslide boundaries and creates strong visual similarity with surrounding land covers. Consequently, accurate and efficient landslide identification from remote sensing imagery remains a significant challenge. To address these challenges, we propose a structural constrained contrastive learning network (SC-Net) for reliable landslide extraction from remote sensing images. First, a multi-structural feature extraction module is designed to capture landslide-specific geometric characteristics. These features are further enhanced by fusing multi-scale semantic representations extracted from a pretrained backbone network through an attention-based adaptive feature fusion module. Additionally, a mask-constrained object-level contrastive learning strategy is introduced to enforce global structural consistency at the landslide object-level, thereby improving the discriminability between landslide and non-landslide regions. Extensive experiments conducted on the publicly available CAS landslide dataset demonstrate the effectiveness of the proposed method. The proposed SC-Net achieves IoU scores of 89.89% and 79.76% on the CAS-UAV and CAS-SAT datasets, respectively, outperforming the best-performing baseline by 2.09% and 0.46%. The proposed method provides an effective solution for large-scale landslide monitoring and demonstrates potential for applications in power transmission corridor inspection and infrastructure safety assessment. Full article

While Japan’s population is declining overall, some areas remain densely populated within designated hazard zones. Understanding how these spatial patterns vary across urban contexts is important for sustainable and resilient urban development. This study presents a nationwide analysis of hazard-specific estimated population exposure at postcode-level population core cells across walkability-based urban spatial characteristics in Japan. We integrated designated hazard-zone layers from the Geospatial Information Authority of Japan (GSI) with 250 m census population grids and linked the resulting dataset to the Japan Postcode-level Walkability Index using these core cells as a common spatial unit. This analysis used postcode-level population core cells and was not designed to estimate total hazard-zone-based exposure within entire postcode areas. The four hazard-zone layers were analyzed separately to characterize hazard-specific patterns, not to assess simultaneous or compound hazard events, cumulative exposure, or compound risk. Population core cells in higher-JPWI strata generally overlapped more frequently with flood, storm surge, and tsunami inundation zones, whereas lower-JPWI population core cells overlapped more frequently with designated landslide warning zones. Where hazard-zone overlap was identified, estimated exposed populations tended to be larger in higher-JPWI core cells. The pattern should be interpreted descriptively because the estimate is partly influenced by cell population and JPWI includes a population-density component. Overall, the results show hazard-specific differences in how walkability-based urban spatial characteristics coincide with hazard-zone-based estimated population exposure, providing a transparent and nationally consistent baseline for characterizing designated hazard-zone overlap and estimated exposed population at population core cells in Japan. 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

Landslides and their associated secondary hazards present substantial threats to both public infrastructure and resident safety. The rapid and accurate identification of large-scale landslide events remains a critical challenge in the field of engineering geology. In this study, a YOLO-based deep learning model is developed for landslide identification relying on a training dataset constructed using the satellite imagery of Longyan City, Fujian Province, in 2024. Adopting the double machine learning model, we examine the causal inference relationships between landslide and causative factors, including rainfall (R), mean Normalized Difference Vegetation Index (NDVI) and Distance to roads (DRoa). A total of 1185 landslides is identified in 2024, covering an area of approximately 31.02 km². The landslides are predominantly concentrated in Shanghang, Wuping, Changting, and the southern part of Xinluo. The landslides mainly correspond to elevations around 300–500 m, slopes among the interval of [10°, 25°], and annual rainfall intensities ranging from 1600 m to 1700 mm. The top five key factors for landslide occurrence in descending order are NDVI, R, DRoa, Distance to Rivers (DRiv) and Aspect (A), in terms SHAP values. Causal inference analysis reveals that the rainfall in June and July shows significant positive causal effects to landslide, which is consistent with the physical mechanism of rainfall-induced landslide and the landslide data reported by the government. The framework proposed and the findings in this study offer valuable technical and theoretical support for landslide identification and risk assessment in southwestern Fujian. Full article

Rapid landslide motions control impact area, flow velocity, deposition pattern, and, in extreme cases, are a river-blocking hazard; therefore, reliable dynamic simulations are of direct importance to engineering–geological hazard assessments. Depth-averaged models provide an efficient framework for simulating large-scale mass movements, but conventional physics-informed neural networks (PINNs) remain challenged with regard to nonlinear flows, which can limit their applicability in landslide analysis. To address these limitations, this study develops a physics-informed residual convolutional network model (PI-RCN) for depth-averaged landslide dynamics. The proposed framework combines sequential residual learning with depth-wise separable convolutions (DSCs) and incorporates physics-based residuals, mass conservation, and hard constraints to preserve physical consistency during time marching. The model is evaluated using a 1+1D frictionless dam-break benchmark, a Hong Kong landslide, and the Yigong rock avalanche. Results show that PI-RCN accurately reproduces the benchmark flow evolution with substantially fewer trainable parameters than a baseline fully connected PINN. In the Hong Kong case, the model demonstrates improved convergence stability and optimization efficiency. In the Yigong case, PI-RCN reproduces the main spatiotemporal evolution and multi-stage velocity variation of a long-runout rock avalanche. These results suggest that PI-RCN provides a useful physics-informed framework for efficient and consistent landslide dynamic simulation. Full article

Rapid and reliable landslide mapping from satellite observations is essential for hazard assessment, emergency response, and reservoir-area risk management, yet automatic segmentation remains challenging in mountainous regions because landslide scars are spectrally heterogeneous, terrain-constrained, morphologically irregular, and frequently confused with other exposed surfaces. This study proposes GeoSlide-XMamba, a terrain-conditioned spectral-topographic boundary-aware state-space network for pixel-wise landslide semantic segmentation. The model first separates Sentinel-2 spectral bands and DEM/slope-derived topographic layers into modality-specific branches, integrates them through spectral-topographic adaptive fusion (STAF++), and then performs terrain-conditioned selective state-space scanning in the XMamba bottleneck. Unlike direct token concatenation, the proposed bottleneck uses terrain descriptors to dynamically weight directional selective scan branches so that long-range feature propagation is guided by slope-related morphology. Boundary-aware decoding, signed-distance supervision, and hard-negative mining are further introduced to improve inventory-oriented geometric quality and suppress common false positives. Experiments were conducted on the Landslide4Sense benchmark using 14-channel multispectral-topographic inputs. Among the compared methods, GeoSlide-XMamba achieved the highest validation performance under a unified five-seed protocol, with precision = 0.729, recall = 0.626, F1-score = 0.673, IoU = 0.507, kappa = 0.666, Boundary-F1 = 0.466, and HD95 = 3.45 pixels. Five-seed experiments produced F1 = 0.673 ± 0.003, IoU = 0.507 ± 0.002, Boundary-F1 = 0.466 ± 0.002, and HD95 = 3.45 ± 0.13 pixels, with a 95% CI of [0.670, 0.676] for F1. Relative to the strong 14-channel concatenation baseline, the proposed model improves mean F1 by 0.045 and reduces HD95 by 1.42 pixels. Expanded qualitative inference on Jinsha River patches indicates that the learned spectral-topographic representation transfers plausibly to high-relief reservoir-canyon terrain. These results show that terrain-conditioned state-space modeling can improve both segmentation accuracy and boundary geometry for remote sensing landslide mapping. Full article