Search Results (3,822)

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article

The Yanshangou landslide, located in the Baihetan Reservoir area, poses severe potential threats to the normal operation of the reservoir due to its distinct deformation characteristics and high sensitivity to reservoir water level fluctuations. This study systematically investigates the geological background, deformation characteristics, stability evolution, and landslide-induced surge hazards of the Yanshangou landslide in the Baihetan Reservoir area. This work only considers the influence of reservoir water level fluctuations, which is the dominant factor controlling the current progressive deformation of the landslide. Field surveys and GNSS/deep displacement monitoring results revealed that the Yanshangou landslide exhibits obvious staged deformation characteristics, and the landslide deformation rate was closely coupled with the dynamic changes in reservoir water level. A slope stability evaluation method integrating the Morgenstern–Price limit equilibrium method and Richard’s equation was established, and the results indicated that the Yanshangou landslide has low saturated permeability. Therefore, its factor of safety (FOS) presents a clear four-stage variation trend in response to reservoir water level fluctuations. A Smoothed Particle Hydrodynamics (SPH)-based numerical model was further developed to simulate the landslide-induced surges under two typical reservoir water level scenarios (815 m and 765 m). The simulation results demonstrated that a high reservoir water level led to more intense surges with greater height and higher velocity, while a low reservoir water level resulted in surges with a wider propagation range along the reservoir bank. The research findings of this study provide a comprehensive theoretical basis and detailed data support for the prevention and mitigation of geological hazards in the Baihetan Reservoir area, and also offer a reference for the hazard management of similar reservoir landslides worldwide. Full article

Rapid landslide susceptibility screening is important for linear engineering projects because long corridors, numerous slope units, limited data, and tight schedules often restrict the use of data-intensive models. This study develops an engineering-oriented reduced-factor screening framework based on the Information Value (IV) model and applies the framework to the Beijing-Zhangjiakou Railway corridor. A conventional 10-factor IV model was first established as the reference model. Reduced-factor models were then screened under the same study area, the same landslide inventory, the same modelling workflow, and the same factor classification scheme. The 10-factor model reached an accuracy of 94.87%. Two reduced five-factor models reached the same accuracy: Slope + Aspect + Elevation + Lithology and Engineering Rock + NDVI, and Slope + Aspect + Elevation + Lithology and Engineering Rock + Distance to Rivers. The comparison shows that the full-factor model can be simplified without loss of validation accuracy when a stable terrain–geological framework is retained and a suitable external factor is added. Because the available inventory contains only 45 landslides and does not distinguish failure mechanisms consistently, the proposed model should be regarded as a preliminary probabilistic screening tool rather than a mechanism-specific prediction model. The proposed framework provides a practical approach for corridor-scale hazard screening under incomplete data conditions. Full article

This study explores the environmental justice issues associated with landfill compensation policies in Bantargebang, Indonesia. Although compensation programs have been implemented for many years, communities living near landfills continue to experience ongoing environmental damage and significant health concerns. Using a qualitative descriptive method, this research explores systemic barriers through in-depth interviews, observations, and water quality analysis. The findings indicate that labeling the program as “Social Assistance” within the Local Government Information System (SIPD) redefines ecological compensation as a fixed form of charity, rather than as a mechanism for genuine environmental restitution. Laboratory data show severe bacteriological contamination, with Total Coliform levels reaching 95%, forcing residents to bear substantial “hidden costs” for clean water, perpetuating a cycle of financial dependence. The growing normalization of health hazards is evident in over 5000 annual cases of acute respiratory infections, and the deadly landslide in March 2026, in which claimed seven lives and injured six others. These incidents underscore the failure of existing remediation approaches to safeguard human dignity and well-being. To address these shortcomings, this study proposes the adoption of an Integrated Compensation Model based on Green Social Work. This model emphasizes structural investment, spatial risk-based indices using quantitative data, and budget coding adjustments within the SIPD. This approach highlights the urgent need to move beyond temporary charitable assistance and instead pursue meaningful environmental justice, while positioning social workers as “Social-Ecological Brokers” who help restore dignity and well-being in communities often treated as “sacrifice zones.” 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

At around 8:40 a.m. on 17 July 2024, the Wanshuitian landslide in the Three Gorges Reservoir Area (TGRA) experienced a deformation failure characterized by thrust load-caused deformations and high-speed sliding. Using geological surveys and unmanned aerial vehicle (UAV) photography, this study divided the Wanshuitian landslide area into five zones: sliding initiation (A1), secondary disintegration (A2), main accumulation (B1), right falling (B2), and left falling (B3) zones. Through monitoring data analysis and GeoStudio-based numerical simulations, this study revealed the mechanisms behind the landslide failure mode characterized by slope sliding approximately along the strike of the rock formation under the coupling effect of hydrological and geological conditions. The results indicate that factors inducing the landslide failure include the geomorphic feature of alternating grooves and ridges, the lithologic assemblage characterized by interbeds of soft and hard rocks, the slope structure with well-developed joints, and the sustained heavy rains in the preceding period. In the Wanshuitian landslide area, mudstone valleys are prone to accumulate rainwater, which can infiltrate directly into the weak interlayers of rock masses and soften the rock masses. Multi-peak rain events with a short time interval serve as a critical factor in groundwater recharge. Within 17 days preceding its failure, the Wanshuitian landslide experienced a superimposed process of heavy and secondary rain events with a short interval (four days). Rainwater from the first heavy rain event failed to completely discharge during the short interval, while the secondary rain event also caused rainwater accumulation. These led to a continuous rise in the groundwater table, a constant decrease in the shear strength of the slope, and ultimately the landslide instability. Since the landslide sliding in the dip direction of the rock formation was impeded, the main sliding direction of the landslide formed an angle of 88° with this direction. This led to a unique failure mode characterized by slope sliding approximately along the strike of the rock formation. Based on these findings, this study proposed characteristics for the early identification of the failure of similar landslides, aiming to provide a robust scientific basis for the monitoring, early warning, and prevention and control of the failure of similar landslides. 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 growing complexity of human systems and the increasing frequency of climate-driven hazards have transformed some disasters from isolated events into cascading phenomena which propagate through critical infrastructure networks, disrupting essential services and amplifying systemic risk. This work examines the impacts of extreme storms and subsequent flooding on critical entities as defined under the new EU Directive (Critical Entities Resilience, CER). This study introduces a structured Critical Entities Disruption Database—Greece (CEDD-GR), as a methodological framework for systematically recording and analysing disruptions to critical entities, and applies it to the case of Storm Daniel (2023), one of the most severe flood events recorded in Greece. The analysis identified direct impacts across eight of the eleven sectors defined in the CER Directive, namely, energy, transport, health, drinking water, wastewater, public administration, digital infrastructure and food production, processing and distribution. A total of 21 different types of critical entities were documented, revealing the mechanisms through which failures affected different subsectors. The results underscore the systemic fragility of critical entities when exposed to extreme storms, compound flooding, and mass wasting processes (landslides, ground subsidence) and highlight the need for integrated resilience planning in line with the CER framework. Full article

The geological environment of southeastern Tibet is characterized by complex tectonics and high climatic sensitivity, and giant accumulation landslides pose significant threats to infrastructure and human safety. This study investigates the Pangcun giant accumulation landslide using SBAS-InSAR (2017–2024), UAV photogrammetry, field investigations, and wavelet coherence analysis to examine its deformation and driving mechanisms. The landslide exhibits continuous, slow deformation with clear spatial heterogeneity, divided into two zones, with the largest displacement occurring in the middle of Zone B. Field evidence is consistent with the InSAR results. Wavelet coherence analysis reveals a lagged response of displacement to precipitation at a timescale of about three months. The landslide’s evolution is controlled by unfavorable topography and fragmented materials, with precipitation as the primary trigger. Human activities (agricultural irrigation and slope-toe road excavation) and seismic disturbances also contribute to its progressive development. 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

Time-series InSAR is an important means for early identification and monitoring of landslides. However, in complex mountainous areas, it still faces challenges such as significant geometric distortions and complicated deformation mechanisms. To address these issues, this paper proposes a landslide identification and prediction framework that integrates ascending and descending orbits InSAR observations with physics-guided deep learning. Taking Yangbi County, Yunnan Province, as a case study, we combined ascending and descending Sentinel-1A data and employed the SBAS-InSAR method to identify potential landslides, detecting a total of 41 hazardous sites. The cumulative displacement time series of typical landslides were further extracted along the slope aspect to more realistically reflect landslide movement characteristics. On this basis, wavelet decomposition was introduced to separate the displacement series into trend and periodic components. Gray relational analysis was then used to select influencing factors such as precipitation and temperature, and a stepwise prediction model based on LSTM (WT-LSTM) was constructed. The results indicate that the model achieves significantly higher prediction accuracy at characteristic points of the representative landslide (RMSE = 1.16–2.19 mm) compared to standalone LSTM and SVR models. These findings demonstrate its effectiveness and potential applicability in landslide deformation monitoring and prediction in complex mountainous areas, while also providing a useful reference for landslide risk early warning. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

Earthquake-induced landslides are a major hazard in mountainous regions, where complex topography and near-surface conditions jointly control ground-motion amplification and slope instability. In this context, ground-motion models used as triggering inputs for landslide analyses must accurately represent site effects in complex terrain. This study develops a geomorphometry-informed ground-motion model based on predictors derived from global remote sensing Digital Elevation Models (DEMs), conceived as a triggering component for earthquake-induced landslide applications. The model is based on the eXtreme Gradient Boosting (XGBoost) regression algorithm and predicts peak ground acceleration, peak ground velocity, and spectral accelerations by integrating seismic source parameters, finite-fault source-to-site metrics, and geomorphometric site proxies derived from global DEMs. The model is trained on an extended Italian strong-motion dataset comprising about 8300 recordings from 90 earthquakes with finite-fault rupture models and is evaluated using a strict leave-one-event-out validation scheme. Results show that finite-fault parameterization reduces prediction errors by about 11% compared to point-source formulations, while DEM-derived site proxies improve predictive performance by approximately 5% relative to V_S30 and 12% relative to the fundamental frequency f₀. Residual analysis yields inter-event variability of 0.19–0.22 and intra-event variability of 0.23–0.26. The proposed framework demonstrates how global remote sensing products provide value-added predictors for ground-motion triggering in complex terrain, suitable for integration with earthquake-induced landslide susceptibility models. Full article

This paper investigates the mechanical transfer mechanism of bimetallic composite pipes used in highly sour gas fields located in high-steep mountainous areas. It systematically analyzes the mechanical response behavior of these pipes under the coupled effects of complex geological conditions and operational loads. By establishing and validating a finite element model that accounts for material nonlinearity and pipe–soil interaction, the study examines the influence of key factors—including internal pressure, landslide displacement, and base pipe wall thickness—on the stress distribution and transfer mechanism within the pipeline. The results demonstrate that increased internal pressure significantly elevates both circumferential and axial stresses: when internal pressure increases from 7 MPa to 9 MPa, the liner hoop stress increases by 35.5% and the base pipe axial stress increases by 27.5%. When landslide displacement exceeds a critical threshold of 3 m, the stress in the base pipe rises sharply, with axial stress increasing by 39.7% when displacement increases from 3 m to 5 m; conversely, increasing the base pipe wall thickness from 12 mm to 15 mm effectively reduces the overall stress level, decreasing base pipe axial stress by 40.4% and liner axial stress by 86.9%. Stress transfer exhibits a dual-path characteristic, which can be described as “bidirectional transfer induced by internal pressure” and “progressive transfer caused by landslide”. These quantitative findings provide a theoretical basis for the safe design and operation of bimetallic composite pipes in high-steep mountainous regions. Full article