Search Results (143)

Landslides occurring on reservoir banks in steep, high-gradient canyon areas pose a significant risk of surge disasters when they slide into the water. This can endanger the lives and property of downstream residents and damage coastal infrastructure. Therefore, researching the formation mechanisms, disaster evolution, and risk assessment of the landslide-surge disaster chain in such areas is essential. This paper takes the Laowuchang landslide in the Shuibuya Reservoir area of the Qingjiang River, China, as its research object. Using GeoStudio 2018 software, it evaluates the landslide’s stability under varying reservoir water levels and rainfall conditions. For potential unstable scenarios identified, a full-chain numerical simulation of the landslide–tsunami disaster was conducted based on the Tsunami Squares method, with a focus on analyzing the wave characteristics during generation, propagation, and run-up processes. Furthermore, the paper assesses the risk of landslide–tsunami disasters in the Laowuchang landslide area. The research findings indicate that: (1) Under the long-term continuous river incision, limestone of the Triassic Daye Formation slides along weak interlayers, inducing large-scale collapses. Subsequently, part of the landslide mass is transported by water, while most accumulates in the near-shore area of the Qingjiang River, ultimately shaping the present morphology of the landslide. (2) The Laowuchang landslide is stable under static water levels of 375 m and 400 m, with corresponding safety factors of 1.137 and 1.167, respectively. Under combined static water level and heavy rainfall conditions, the slope stability decreases significantly, with safety factors of 1.034 and 1.064, respectively. Under reservoir drawdown conditions, the slope tends to be unstable, with a safety factor of 1.047. (3) Numerical simulation results indicate that if the Laowuchang landslide fails into water by the speed of 12 m/s and with a volume of 2 million m³, the maximum initial wave height can reach 15.9 m. The tsunami’s affected range spans 10 km upstream and downstream from the landslide mass, with four houses and one substation within a 2 km up and downstream falling into high-risk areas. If abnormal increases in landslide displacement occur, relocation and risk avoidance measures should be implemented. The findings of this study provide a scientific basis for the prevention and response to landslide–tsunami disasters in similar high and steep canyon terrains. Full article

Rapid and accurate mapping of earthquake-triggered landslides from satellite imagery is critical for emergency response and hazard assessment, yet remains challenging due to irregular boundaries, extreme size variations, and atmospheric noise. This paper proposes BiFusion-LDSeg, a novel bi-directional fusion enhanced latent diffusion framework that synergistically combines CNN-Transformer architectures with generative diffusion models for robust landslide segmentation. The framework introduces three key innovations: (1) a dual-encoder with Bi-directional Attention Gates (Bi-AG) enabling sophisticated cross-modal feature calibration between local CNN textures and global Transformer context; (2) a conditional latent diffusion process operating in learned low-dimensional landslide shape manifolds, reducing computational complexity by 100× while enabling inference with only 10 sampling steps versus 1000+ in standard diffusion models; and (3) a boundary-aware progressive decoder employing multi-scale reverse attention mechanisms for precise boundary delineation. Comprehensive experiments on three earthquake datasets from Sichuan Province, China (Lushan Mw 7.0, Jiuzhaigou Mw 6.5, Luding Mw 6.8) demonstrate superior performance, outperforming state-of-the-art methods by 7–13% in IoU and 5–7% in DSC across all three datasets. The framework exhibits exceptional noise robustness, strong cross-dataset generalization, and inherent uncertainty quantification, enabling reliable deployment for post-earthquake landslide inventory mapping at regional scales. Full article

The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images become blurred over time, leading to huge challenges in detection. Considering that old landslides exhibit obvious shape characteristics, we propose ResU-SPMNet, a deep learning model that integrates shape characteristics into the baseline ResU-Net. The proposed model consists of three components: ResU-Net, shape prior module (SPM), and the atrous spatial pyramid pooling (ASPP) module, which jointly enhance segmentation performance from the perspectives of shape constraints and multi-scale feature representation. To validate the effectiveness of the proposed approach, old landslides in representative regions of the Loess Plateau were selected as the study targets. Results show that the proposed model outperforms ResU-Net, SegNet, MultiResUnet, and DeepLabv3+ in old landslide segmentation, achieving an F1-score of 0.6669 and an MCC of 0.6167. Moreover, generalization tests conducted in independent regions indicate that the model exhibits strong robustness across different seasons. The best performance is achieved in summer, whereas performance declines in winter due to adverse factors such as reduced illumination and snow or ice cover. Full article

Sustainable landslide risk management is critical for achieving resilient communities and supporting the United Nations Sustainable Development Goals, particularly in vulnerable mountainous regions of developing countries. This study presents experimental evidence supporting dimensionless analysis approaches for characterizing granular flow behavior, contributing to cost-effective landslide hazard assessment frameworks. We designed a 4 m experimental flume to investigate the influence of particle characteristics on flow velocity and runout distance, using two materials with contrasting shapes but similar density (~460 kg/m³) and nominal size (~5 mm): uniform crystal beads (φ = 25.2°) and non-uniform crushed granite particles (φ = 36.9°). High-resolution imaging (30 fps, 2336 × 1752 pixels) captured 30 flow experiments from initiation to deposition. Results demonstrate significant differences in flow behavior: crystal beads achieved 50% longer runout distances and 46% higher maximum velocities (380 cm/s vs. 260 cm/s) compared to granite particles. The Savage number ($N_{sav}$) effectively captured fundamental flow-regime differences, with granite particles exhibiting values seven times lower than crystal beads (3.69 vs. 23.91, p < 0.001), indicating greater frictional energy dissipation relative to collisional energy transfer. The Bagnold number confirmed inertially dominated regimes ($N_{Bag}$ > 10⁶) with negligible viscous effects in both materials. These findings demonstrate that accessible material characterization using standard triaxial testing and dimensionless analysis can significantly improve landslide runout prediction accuracy, supporting evidence-based decision-making for sustainable territorial planning and community protection. This research supports the development of practical risk assessment methodologies implementable in resource-limited settings, promoting sustainable development through improved natural hazard management. Full article

This study addresses the challenge of predicting runout distance of rainfall-induced shallow landslides by integrating deep learning and explainable machine learning. Using the June 2024 landslide disaster at the Fujian-Guangdong-Jiangxi border as a case study and remote sensing images as the data source, we developed an improved U-Shaped Convolutional Neural Network model (RAC-Unet) combining Deep Residual Structure, Atrous Spatial Pyramid Pooling, and Convolutional Block Attention Module modules. The model identified 34,376 shallow landslides and built a dynamic parameter database with 8875 samples, which was used for data-driven model training. After comparing models, Extreme Gradient Boosting was chosen as the best (R² = 0.923), with its performance confirmed by Wilcoxon analysis and good generalization in external validation (R² = 0.877). SHapley Additive Explanations analysis revealed how factors like the area of the sliding source zone (SA), length/width ratio of the sliding source zone (SLWR), and average slope of the source zone (SS) affect landslide runout, a simplified model using the three parameters SA, SLWR, and SS was constructed (R² = 0.862). Compared to traditional models, this integrated framework solves the pre-disaster impact range estimation problem, deepens understanding of shallow landslide dynamics, and enables accurate pre- and post-disaster predictions. It provides comprehensive support for disaster risk assessment and emergency response in southeastern hilly areas. Full article

Faults are the primary drivers of earthquakes and exert a strong control on rupture mechanisms, earthquake magnitude, and the spatial distribution of coseismic landslides (CLs). However, how CL spatial distribution patterns vary with faulting style remains poorly constrained. Here, we compiled a catalog of CLs associated with 18 global major earthquakes (M_W > 6.0) within continental regions since 1900 and explored the distribution patterns of CLs associated with the three major earthquake types: oblique-slip, dip-slip, and strike-slip. Our results reveal two distinct spatial distribution patterns of CLs: a hanging-wall distribution for oblique-slip and dip-slip earthquakes and a bell-shaped distribution for strike-slip earthquakes. The orientation of CLs is closely related to fault geometry and slip type. Specifically, in oblique-slip, strike-slip, and dip-slip earthquakes, CLs predominantly develop parallel, perpendicular, or perpendicular to the fault strike, respectively. In terms of slip rake, CLs are mainly aligned perpendicular, parallel, and parallel to the fault slip direction for oblique-slip, strike-slip, and dip-slip events, respectively. Importantly, the distribution patterns of CLs encode information about ground movement during an earthquake. While Peak Ground Acceleration (PGA) serves as an indicator of ground motion intensity, a comprehensive characterization of CLs—including their size and predominant movement direction—requires consideration of both the earthquake type and the local slope conditions. Full article

The incidence of infectious diseases in children may be affected by climate change-related disaster risks that increase as extreme weather events become more frequent. Therefore, this research aims to diagnose the impact of such disaster risks on the disease incidence, focusing on diarrhoea, dengue haemorrhagic fever (DHF), and acute respiratory infection (ARI), commonly experienced by children. To accomplish this task, we construct integer-valued autoregressive (INAR) models for the number of disease cases among children in several age groups, with an overdispersed distributional assumption to account for its variability that exceeds its central tendency. Additionally, we include the numbers of floods, landslides, and extreme weather events at previous times as explanatory variables. In particular, we consider a case study in Indonesia, a tropical country highly vulnerable to the aforementioned climate change-related diseases and disasters. Using monthly data from January 2010 to December 2024, we find that the incidence of diarrhoea in children is positively impacted by landslides (but negatively affected by floods and extreme weather events). Landslides, frequently caused by excessive rainfall, also increase DHF incidence. Furthermore, the increased incidence of ARI is driven by extreme weather conditions, which are more apparent during and after COVID-19. These findings offer insights into how climate scenarios may increase children’s future health risks. This helps shape health strategies and policy responses, highlighting the urgent need for preventive measures to protect future generations. Full article

Landslides and rockfalls can negatively impact human activities and cause radical changes to the surrounding environment. For example, they can destroy entire buildings and roadway infrastructure, block waterways and create sudden dams, resulting in upstream flooding and increased flood risk downstream. In extreme cases, they can even cause loss of life. External factors such as weathering, vegetation and mechanical stress alterations play a decisive role in their evolution. These actions can reduce strength, which can have an adverse impact on the slope’s ability to withstand failure. For rockfalls, this process also affects fragmentation, creating variations in the size, shape and volume of detached blocks, which influences propagation and impact on the slope. In this context, the Morino-Rendinara landslide is a clear example of rockfall propagation influenced by fragmentation. In this case, fragmentation results from tectonic stresses acting on the materials as well as specific climatic conditions affecting rock mass properties. This study explores how different fragmentation scales influence both velocity and landslide propagation along the slope. Using numerical models, based on lumped mass approach and stochastic analyses, various scenarios of rock material fracturing were examined and their impact on runout was assessed. Different scenarios were defined, varying only the fragmentation degree and different random seed sets at the beginning of simulations, carried out using the Rock-GIS tool. The results suggest that rock masses with high fracturing show reduced cohesion along joints and cracks, which significantly lowers their shear strength and makes them more prone to failure. Increased fragmentation further decreases the bonding between rock blocks, thereby accelerating landslide propagation. Conversely, less fragmented rocks retain higher resistance, which limits the extent of movement. These processes are influenced by uncertainties related to the distribution and impact of different alteration grades, resulting from variable tectonic stresses and/or atmospheric weathering. Therefore, a stochastic distribution model was developed to integrate the results of all simulations and to reconstruct both the landslide propagation and the evolution of its deposits. This study emphasizes the critical role of fragmentation and the volume involved in rockfalls and their runout behaviour. Furthermore, the method provides a framework for enhancing risk assessment in complex geological environments and for developing mitigation strategies, particularly regarding runout distance and block size. Full article

Nieuwerkerk Volcano, located in the Banda Sea, Indonesia, is a submarine volcano whose entire edifice lies beneath sea level. Its proximity to several inhabited islands raises significant concerns regarding potential impacts from future volcanic hazards. Despite historical unrest recorded in 1925 and 1927, a comprehensive geological and geophysical understanding of Nieuwerkerk remains notably limited, with the last research expedition being in 1930. This study seeks to advance our understanding of the geomorphological structure and subsurface characteristics of the region, contributing to a preliminary hazard assessment and delineating key directions for future geoscientific investigation. The data were obtained during our most recent expedition conducted in 2022. High-resolution multibeam bathymetry data were analyzed to delineate the volcano’s morphology, while marine magnetic survey data were processed to interpret magnetic anomalies associated with its structure beneath volcano. Our updated morphological analysis reveals the following: (1) Nieuwerkerk Volcano is among the largest submarine volcanic edifices in the Banda Sea (length = 80 km, width = 30 km, height = 3460 m); (2) there is the presence of twin peaks (depth~300m); (3) there are indications of sector collapse (diameter = 10–12 km); (4) there are significant fault lineaments; and (5) there are landslide deposits, suggesting a complex volcanic edifice shaped by various constructive and destructive processes. The magnetic data show a low magnetic anomaly beneath the surface, where one of the indications is the presence of active magma. These findings significantly enhance our understanding of Nieuwerkerk’s current condition and volcanic evolution for an initial assessment of potential hazards, including future eruptions, edifice collapse, and landslides, which could subsequently trigger tsunamis. Further investigation, including comprehensive geophysical surveys covering the entire Nieuwerkerk area, rock sample analysis, visual seafloor observation, and seawater characterization, is crucial for a comprehensive understanding of its magmatic system and a more robust hazard assessment. This research highlights the critical need for detailed investigations of active submarine volcanoes, particularly those with sparse historical records and close proximity to populated areas, within tectonically complex settings such as the Banda Sea. Full article

In the southeast of China, seasonal rainfall intensity is high, the distribution of mountains and hills is extensive, and many small-scale, shallow landslides frequently occur after consecutive seasons of heavy rainfall. High-precision automated identification systems can quickly pinpoint the scope of the disaster and help with important decisions like evacuating people, managing engineering, and assessing damage. Many people have designed systems for detecting such shallow landslides, but few have designed systems that combine high resolution, high automation, and real-time capability of landslide identification. Taking accuracy, automation, and real-time capability into account, we designed an automatic rainfall-induced shallow landslide detection system based on deep learning and Unmanned Aerial Vehicle (UAV) images. The system uses UAVs to capture high-resolution imagery, the U-Net (a U-shaped convolutional neural network) to combine multi-scale features, an adaptive edge enhancement loss function to improve landslide boundary identification, and the development of the “UAV Cruise Geological Hazard AI Identification System” software with an automated processing chain. The system integrates UAV-specific preprocessing and achieves a processing speed of 30 s per square kilometer. It was validated in Wanli District, Nanchang City, Jiangxi Province. The results show a Mean Intersection over Union (MIoU) of 90.7% and a Pixel Accuracy of 92.3%. Compared with traditional methods, the system significantly improves the accuracy of landslide detection. Full article

Active continental margins, generally characterized by narrow shelves incised by canyons, are pervasively shaped by submarine landslides that can occur near coastal areas. In this context, creating landslide susceptibility maps is the first step in landslide geohazard assessment. This paper focuses on shallow-water submarine landslides along the Capo d’Orlando continental margin and presents a related susceptibility map using the Weight of Evidence method. This method quantifies the strength of the association between a landslide inventory and predisposing factors. A geomorphological analysis of the continental shelf and upper slope yielded a landslide inventory of 450 initiation points, which were combined with five specifically selected preconditioning factors. The results revealed that the most favourable conditions for shallow-water landslides include slopes between 5° and 15°, proximity to faults (<1 km), proximity to river mouths (<2 km), the presence of consolidated lithologies and sandy terraces, and slopes facing NE and E. The landslide susceptibility map indicates that susceptible areas are in canyon heads and flanks, as well as in undisturbed slope portions near canyon heads where retrogressive landslides are likely. The model results are robust (AUC = 0.88), demonstrating that this method can be effectively applied in areas with limited geological data for preliminary susceptibility assessments. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

This research aims to characterize sediment dynamics in the Rupinaro catchment, a uniquely terraced and human-shaped basin in Italy’s Liguria region, employing geomorphometric methods to unravel sediment connectivity in a landscape vulnerable to shallow landslides. Within a scenario-based approach, we utilized high-resolution LiDAR-derived digital terrain models (DTMs) to calculate the Connectivity Index, comparing sediment dynamics between the original terraced landscape and a virtual natural scenario. To reconstruct a pristine slope morphology, we applied a topographic roughness-based skeletonization algorithm that simplifies terraces into linear features to simulate natural hillslope conditions and remove anthropogenic structures. The analysis was carried out considering diverse targets (e.g., hydrographic networks, road networks) and the effect of land use. The results reveal significant differences in sediment connectivity between the anthropogenic and natural morphologies, with implications for erosion and landslide susceptibility. The findings reveal that sediment connectivity is moderately higher in the scenario without terraces, indicating that terraces function as effective barriers to sediment transfer. This highlights their potential role in mitigating landslide susceptibility on steep slopes. Additionally, the results show that roads exert a stronger influence on the Connectivity Index, significantly altering flow paths. These modifications appear to contribute to increased landslide susceptibility in adjacent areas, as reflected by the higher observed landslide density within the study region. Full article

Although Bangladesh is frequently regarded as ‘ground zero’ for climate change, the Chittagong Hill Tracts (CHTs) have only recently been acknowledged for their environmental vulnerabilities, especially after the devastating rainfall and landslides of 2017. However, attributing these risks solely to climate change overlooks their entanglement with structural inequalities, extractive development, deforestation, and long-standing marginalization. The study examines how climate variability intersects with broader environmental risks through a mixed-methods approach, integrating 30 years of NASA TRMM_3B42_daily rainfall data with a household survey (n = 400), life stories, focus group discussions, and key informant interviews conducted across all three CHT districts. Findings do not support a singular attribution to climate change. Rather, they reveal compounded vulnerabilities shaped by land degradation, water scarcity, flash flooding, and landslides—often linked to deforestation and neoliberal development interventions. We argue that the CHT exemplifies ecological entanglement, shaped by climate variability and structural inequalities rooted in land governance and Indigenous dispossession. By integrating spatially disaggregated climate data with historically grounded local experiential narratives, this study contributes to climate justice debates through relational, place-based understandings of vulnerability in the Global South. Full article

Urban expansion in coastal areas involves infrastructure development, industrial growth, and mining activities. These coastal environments face various environmental and geological hazards that require geo-engineers to devise solutions. An integrated geophysical approach aims to address such complex challenges as sea level rise, sea water intrusion, shoreline erosion, landslides and previous anthropogenic activity in coastal settings. In this study, the proposed methodology involves the systematic application of geophysical methods (FDEM, 3D GPR, 3D ERT, seismic), starting with a broad-scale survey and then proceeding to a localized exploration, in order to identify lithostratigraphy, bedrock depth, sea water intrusion and detect anthropogenic buried features. The critical aspect is to leverage the unique strengths and limitations of each method within the coastal environment, so as to derive valuable insights for survey design (extension and orientation of measurements) and data interpretation. The coastal zone of Throrikos valley, Attica, Greece, serves as the test site of our geophysical investigation methodology. The planning of the geophysical survey included three phases: The application of frequency-domain electromagnetic (FDEM) and 3D ground penetrating radar (GPR) methods followed by a 3D electrical resistivity tomography (ERT) survey and finally, using the seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW). The FDEM method confirmed the geomorphological study findings by revealing the paleo-coastline, superficial layers of coarse material deposits and sea water preferential flow due to the presence of anthropogenic buried features. Subsequently, the 3D GPR survey was able to offer greater detail in detecting the remains of an old marble pier inland and top layer relief of coarse material deposits. The 3D ERT measurements, deployed in a U-shaped grid, successfully identified the anthropogenic feature, mapped sea water intrusion, and revealed possible impermeable formation connected to the bedrock. ERT results cannot clearly discriminate between limestone or deposits, as sea water intrusion lowers resistivity values in both formations. Finally, SRT, in combination with MASW, clearly resolves this dilemma identifying the lithostratigraphy and bedrock top relief. The findings provide critical input for engineering decisions related to foundation planning, construction feasibility, and preservation of coastal infrastructure. The methodology supports risk-informed design and sustainable development in areas with both natural and cultural heritage sensitivity. The applied approach aims to provide a complete information package to the modern engineer when faced with specific challenges in coastal settings. Full article