Search Results (3,544)

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Medium and heavy rare earths (REEs) are mainly from weathered crust elution-deposited rare earth ores (WREOs), where REEs are adsorbed in ionic form on the surface of clay minerals such as kaolinite, illite, halloysite, etc. REEs in WREOs are extracted through the in situ leaching process with (NH₄)₂SO₄ solution via ion exchange. However, this process often results in the swelling of clay minerals, subsequently destroying the ore body structure and causing landslides. This study investigated the inhibitory effects of humic acid (HA) on the swelling of primary clay minerals. An optimal inhibition on the swelling of clay minerals was demonstrated at 0.2 g/L. HA was mixed with 0.1 mol/L (NH₄)₂SO₄ solution at the solution pH of 6.8 and temperature of 25 °C. The swelling efficiency of kaolinite, illite, and halloysite in presence of HA decreased by 0.29%, 1.19%, and 0.19%, respectively, compared to using (NH₄)₂SO₄ alone. The surface hydration parameter of clay minerals was further calculated through viscosity theory. It was demonstrated that the surface hydration parameter of kaolinite and halloysite decreased nearly threefold, while that of illite decreased fivefold, demonstrating a desirable inhibition on clay swelling with HA. Viscosity theory offers valuable theoretical support for the development of anti-swelling agents. Full article

This article presents the ADAImpact tool, a QGIS plugin designed to assess the potential impacts of geohazards—such as landslides, subsidence, and sinkholes—using open-access surface displacement data from the European Ground Motion Service (EGMS), which is based on Sentinel-1 satellite observations. Created as part of the European RASTOOL project, ADAImpact integrates InSAR-derived ground movement data with exposure datasets (including population, infrastructure, and buildings) to support civil protection agencies in conducting risk assessments and planning emergency responses. The tool combines “Process Magnitude”, with “Exposure” metrics, quantifying the population and critical infrastructure affected, to generate potential impact maps for ground motion hazards. When applied to case studies along the Portugal–Spain border and the coastal region of Granada, Spain, ADAImpact successfully identified areas of high potential impact. These results underscore the tool’s utility in pre- and post-disaster assessment, highlighting its potential for scalability across Europe. Full article

Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users. Full article

The DD Guyot, a flat-topped seamount located in the Western Pacific, was completely mapped using multibeam echosounders (MBESs) in 2024. Clarifying substratum patterns is crucial for understanding seafloor evolution, sediment transport processes, and resource assessment. This study integrates near-bottom video data from the manned submersible Jiaolong, multibeam bathymetry and backscatter data from EM124, and a convolutional neural network (CNN) model to classify the four substratum types (exposed bedrock, thinly sedimented bedrock, sediment–rock transition zone, and continuous sediment) of the DD Guyot. The results indicate that exposed bedrock predominates on the summit platform, while sediment cover increases with water depth along the flank. The base of the guyot is almost entirely covered by sediments. Two landslide areas were identified, with clear main scarps, sidewalls, and debris accumulations. These features, together with underflow erosion, collectively influence sediment distribution patterns. The resulting substratum maps provide guidance for seabed resource exploration. The results are consistent with a post-drowning onlap framework, which points to a drowning unconformity, but video and surface acoustic data alone are insufficient for definitive confirmation. Further investigation is required to more clearly elucidate the substratum characteristics of the DD Guyot. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

The Rif Mountains in northern Morocco are highly exposed to geohazards, particularly earthquakes and mass movements. In this context, the Zoumi region is most affected, showing various mass movement types involving both unconsolidated and solid materials. This study evaluates the region’s susceptibility to mass movements using logistic regression (LR), applied for the first time in this area. The model incorporates eight key predisposing factors known to influence mass movement: slope gradient, slope aspect, land use, drainage density, elevation, lithology, fracturing density, and earthquake isodepths. Historical mass movements were mapped using remote sensing and field surveys, and statistical analysis calculation was conducted to analyze their spatial correlation with these environmental conditioning factors. A mass movement susceptibility (MMS) map was produced, classifying the region into four susceptibility levels, ranging from low to very high. Landslides were the most frequent movement type (36%). The LR model showed strong predictive performance, with an AUC of 88%, confirming its robustness. The final map reveals that 42% of the Zoumi area falls within the high to very high susceptibility zones. These results highlight the importance of using advanced modeling approaches to support risk mitigation and land use planning in environmentally sensitive mountain regions. Full article

Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems. Full article

Landslides are common in mountainous regions and can significantly affect human life and infrastructure. The aim of this study is to analyze the role of hydrothermally altered rocks in generating ground instability and triggering debris flows in the Canoas microbasin, Sierra de Angangueo, within the Monarch Butterfly Biosphere Reserve. We characterized the unaltered (andesite) and altered (andesitic breccia) rocks from the landslide scarp through fieldwork and laboratory analysis. The altered rock exhibited an extremely low simple compressive strength of 0.47 ± 0.05 MPa. In contrast, the unaltered rock exhibited a higher strength of 36.26 ± 18.62 MPa and lower porosity. Petrographic analysis revealed that the unaltered rock primarily consists of an andesitic groundmass with plagioclase and orthopyroxene phenocrysts partially altered to sericite and kaolin. In comparison, the altered rock contains a matrix rich in clay, iron oxides, and completely replaced phenocrysts. The andesitic breccia has a high proportion of clay and silt and displays soil-like mechanical properties, making it vulnerable to saturation collapse during heavy rainfall. This research offers valuable insights into geological risk management in mountainous volcanic regions. The findings demonstrate that the presence of hydrothermally altered andesitic breccia with weak geomechanical properties was the critical factor that triggered the Canoas debris flow, underscoring hydrothermal alteration as a key control of slope instability in volcanic settings. Full article

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article

This study examines the kinematic behavior of a large-scale colluvial landslide in a coastal low-elevation forest, where rainfall, geological formations, and hydrological conditions drive substantial slope displacement. The landslide comprises a colluvial layer overlying mudstone, with downslope movement toward the coastline induced by gravitational forces and infiltration. Using GPS surveys, inclinometers, soil moisture sensors, and numerical modeling, the temporal and spatial patterns of displacement were analyzed. Maximum horizontal displacements reach 8.1 cm/year, with deep-seated movements extending over 25 m into the mudstone. Key mechanisms include weakening of the colluvium–mudstone interface and creep within saturated mudstone, while a hydraulic barrier near the coastline restricts subsurface flow. Progressive upslope migration of the freshwater-bearing mudstone zone under annual rainfall further contributes to long-term deformation. These findings provide critical insights into the hydrologically controlled kinematics of coastal colluvial landslides. Full article

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education. Full article

Landslide runout distance is governed not only by collapsed magnitude but also by site-specific geoenvironmental conditions. While remote sensing techniques has advanced landslide susceptibility mapping, its application to runout modeling remains limited. This study examined the role of collecting data on various site conditions through remote sensing and field surveys datasets in predicting the landslide travel distance from the 2022 disaster in Petrópolis, Rio de Janeiro. A total of 218 multivariate linear regression models were developed using morphometric, remote sensing, and field survey variables collected across collapse, transport, and deposition zones. Results show that predictive accuracy was limited when based solely on landslide scale (R² = 0.06–0.10) but improved substantially with the inclusion of site condition data across collapse, transport, and deposition zones (R² = 0.49–0.51). Additionally, model performance was strongly influenced by runout path typology, with channelized flows producing the most stable and accurate predictions (R² = 0.73–0.90), while obstructed and open-slope paths performed worse (R² = 0.39–0.61). These findings demonstrate that empirical models integrating multizonal site-condition data and runout path typology offer a scalable, reproducible framework for landslide hazard mapping in data-scarce, complex mountainous urban environments. Full article

The geological environment is characterized by continuous dynamic changes. Landslide activity characteristics can reflect the geological environmental background that affects the landslide development in different historical periods. A comprehensive methodology framework for landslide susceptibility assessment based on landslide activity is proposed. The core concept involves classifying landslide samples into active and inactive categories. Focusing on the Baiyu–Batang section of the upper Jinsha River in the Qinghai–Tibet Plateau, the influence of landslide activity characteristics on landslide susceptibility assessment is investigated. Both ancient and recent landslides are widely distributed. A total of 366 landslides are identified, which are categorized into three subsets: Dataset A (190 active landslides), Dataset B (190 active and 176 inactive landslides), and Dataset C (176 inactive landslides). Eight disaster-causing factors are selected, and the weighted information value model is utilized to perform the landslide susceptibility assessment. Results show that regions exhibiting very high and high landslide susceptibility are mainly situated along riverbanks such as the Jinsha River, Baqu River, and Ouqu River, exhibiting a distinct linear distribution pattern aligned with the river systems. The landslide susceptibility based on Dataset A demonstrates the highest accuracy, suggesting that incorporating landslide activity significantly enhances the reliability of landslide susceptibility assessment in the current geological environment. Full article

With climate change, the Qinghai–Tibet Highway (QTH) is facing increasingly severe risks of natural hazards, posing a significant threat to its normal operation. However, the types, distribution, and future risks of hazards along the QTH are still unclear. In this study, we established an inventory of multi-hazards along the QTH by remote sensing interpretation and field validation, including landslides, debris flows, thaw slumps, and thermokarst lakes. The QTH was segmented into three sections based on hazard distribution and environmental factors. Susceptibility modelling was performed for each hazard within each section using machine learning models, followed by further evaluation of hazard susceptibility under future climate change scenarios. The results show that, at present, approximately 15.50% of the area along the QTH exhibits high susceptibility to multi-hazards, with this proportion projected to increase to 20.85% and 23.32% under the representative concentration pathways (RCP) 4.5 and RCP 8.5 distant future scenarios, respectively. Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Gravity hazards demonstrate limited sensitivity to climate change, whereas thermal hazards exhibit a more pronounced response. Our geomorphology-based segmented assessment framework effectively enhances evaluation accuracy and model interpretability. The results can provide critical insights for the operation, maintenance, and hazard risk management of the QTH. Full article