Search Results (166)

Earthquake-triggered landslides pose significant hazards to lives and infrastructure. While existing seismic landslide models primarily focus on seismic and terrain variables, they often overlook the dynamic nature of hydrologic conditions, such as seasonal soil moisture variability. This study addresses this gap by incorporating satellite-based soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission into the assessment of seismic landslide occurrence. Using landslide inventories from five major earthquakes (Nepal 2015, New Zealand 2016, Papua New Guinea 2018, Indonesia 2018, and Haiti 2021), a balanced global dataset of landslide and non-landslide cases was compiled. Exploratory analysis revealed a strong association between elevated pre-event soil moisture and increased landslide occurrence, supporting its relevance in seismic slope failure. Moreover, a Random Forest model was trained and tested on the dataset and demonstrated excellent predictive performance. To assess the generalizability of the model, a leave-one-earthquake-out cross-validation approach was also implemented, in which the model trained on four events was tested on the fifth. This approach outperformed comparable models that did not consider soil moisture, such as the United States Geological Survey (USGS) seismic landslide model, confirming the added value of satellite-based soil moisture data in improving seismic landslide susceptibility assessments. Full article

Addressing the technical demands for the rapid, precise detection of earthquake-triggered landslides in loess tablelands, this study proposes and validates an innovative methodology integrating enhanced deep learning architectures with large-tile processing strategies, featuring two core advances: (1) a critical enhancement of YOLOv8’s shallow layers via a higher-resolution P2 detection head to boost small-target capture capabilities, and (2) the development of a large-tile segmentation–tile mosaicking workflow to overcome the technical bottlenecks in large-scale high-resolution image processing, ensuring both timeliness and accuracy in loess landslide detection. This study utilized 20 km² of high-precision UAV imagery acquired after the 2023 Gansu Jishishan Ms 6.2 earthquake as foundational data, applying our methodology to achieve the rapid detection and precise segmentation of landslides in the study area. Validation was conducted through a comparative analysis of high-accuracy 3D models and field investigations. (1) The model achieved simultaneous convergence of all four loss functions within a 500-epoch progressive training strategy, with mAP50(M) = 0.747 and mAP50-95(M) = 0.46, thus validating the superior detection and segmentation capabilities for the Jishishan earthquake-triggered loess landslides. (2) The enhanced algorithm detected 417 landslides with 94.1% recognition accuracy. Landslide areas ranged from 7 × 10⁻⁴ km² to 0.217 km² (aggregate area: 1.3 km²), indicating small-scale landslide dominance. (3) Morphological characterization and the spatial distribution analysis revealed near-vertical scarps, diverse morphological configurations, and high spatial density clustering in loess tableland landslides. Full article

The Mw 7.8 Gorkha Earthquake of 25 April 2015 triggered over 25,000 landslides across central Nepal, with 4775 events concentrated in Gorkha District alone. Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. Using a balanced dataset of 4775 landslide and non-landslide instances, we evaluated the performance of Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) models through spatial cross-validation, SHapley Additive exPlanations (SHAP) explainability, and ablation analysis. The RF model outperformed all others, achieving an accuracy of 87.9% and a Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) value of 0.94, while XGBoost closely followed (AUC = 0.93). Ensemble models collectively classified over 95% of observed landslides into High and Very High susceptibility zones, demonstrating strong spatial reliability. SHAP analysis identified elevation, proximity to fault, peak ground acceleration (PGA), slope, and rainfall as dominant predictors. Notably, the inclusion of post-seismic rainfall substantially improved recall and F1 scores in ablation experiments. Spatial cross-validation revealed the superior generalizability of ensemble models under heterogeneous terrain conditions. The findings underscore the value of integrating post-seismic hydrometeorological factors and spatial validation into susceptibility assessments. We recommend adopting ensemble models, particularly RF, for operational hazard mapping in earthquake-prone mountainous regions. Future research should explore the integration of dynamic rainfall thresholds and physics-informed frameworks to enhance early warning systems and climate resilience. Full article

The reduction in disaster risk in urban regions due to natural hazards (e.g., earthquakes, landslides, floods, and tropical cyclones) is primarily a development matter that must be treated within the scope of a broader urban development framework. Natural hazard assessment is one of the turning points in mitigating disaster risk, which typically contributes to stronger urban resilience and more sustainable urban development. Regarding this challenge, our research proposes a new approach in the signal processing chain and feature extraction from microtremor data that focuses mainly on the Horizontal-to-Vertical Spectral Ratio (HVSR) so as to assess liquefaction potential as a natural hazard using AI. The key raw seismic features of site amplification and resonance are extracted from the data via bandpass filtering, Fourier Transformation (FT), the calculation of the HVSR, and smoothing through the use of moving averages. The main novelty is the integration of machine learning, particularly stacked ensemble learning, for liquefaction potential classification from imbalanced seismic datasets. For this approach, several models are used to consider class imbalance, enhancing classification performance and offering better insight into liquefaction risk based on microtremor data. Then, the paper proposes a liquefaction detection method based on deep learning with an autoencoder and stacked classifiers. The autoencoder compresses data into the latent space, underlining the liquefaction features classified by the multi-layer perceptron (MLP) classifier and eXtreme Gradient Boosting (XGB) classifier, and the meta-model combines these outputs to put special emphasis on rare liquefaction events. This proposed methodology improved the detection of an imbalanced dataset, although challenges remain in both interpretability and computational complexity. We created a synthetic dataset of 1000 samples using realistic feature ranges that mimic the Rif data region to test model performance and conduct sensitivity analysis. Key seismic and geotechnical variables were included, confirming the amplification factor (Af) and seismic vulnerability index ($K_g$) as dominant predictors and supporting model generalizability in data-scarce regions. Our proposed method for liquefaction potential classification achieves 100% classification accuracy, 100% precision, and 100% recall, providing a new baseline. Compared to existing models such as XGB and MLP, the proposed model performs better in all metrics. This new approach could become a critical component in assessing liquefaction hazard, contributing to disaster mitigation and urban planning. Full article

This study focuses on the investigation of the Tepehan landslide triggered by the 6 February 2023, Kahramanmaraş earthquake in Türkiye. The overall goal of this study is to understand the slope condition and simulate the failure considering pre- and post-event geometry. Topographic variations in the landslide area were analyzed using digital elevation models (DEMs) derived from the Sentinel-1 Synthetic Aperture Radar (SAR) satellite data and geospatial analysis. Slope stability analyses were conducted over a representative alignment, including assessments of soil structure, geological history, and field features. A limit equilibrium back-analysis was performed under both static and pseudo-static conditions, where an earthquake load coefficient was considered in the analyses. A total of five scenarios were evaluated to determine factors of safety (FoS) based on fully softened and residual strength parameters. The resulting critical slip surfaces from the simulations were compared with the geomorphometric analysis, necessitating the adjustment of the subsurface hard clay layer for residual conditions. The analyses revealed that the slope behaves as a delayed first-time landslide, with bedding planes acting as localized weak layers, reducing mobilized shear strength. This integrated remote sensing–geotechnical approach advances landslide hazard evaluation by enhancing the precision of slip surface identification and post-seismic slope behavior modeling, offering a valuable framework for similar post-disaster geohazard assessments. Full article

This study investigates co-seismic landslides triggered by the 1 January 2024 Mw 7.6 Noto Peninsula earthquake in Japan using LiDAR differentiation and a modified Savage–Hutter model. By analyzing pre- and post-earthquake high-resolution topographic data from 13 landslides in a geologically homogeneous area of the peninsula, we characterized distinct landslide morphologies and dynamic behaviours. Our approach combined static morphological analysis from LiDAR data with simulations of granular flow mechanics to evaluate landslide mobility. Results revealed two distinct landslide types: those with clear erosion-deposition zonation and complex landslides with discontinuous topographic changes. Landslide dimensions followed power-law relationships (H = 7.51L^0.50, R² = 0.765), while simulations demonstrated that internal deformation capability (represented by the μ parameter) significantly influenced runout distances for landslides terminating on low-angle surfaces but had minimal impact on slope-confined movements. These findings highlight the importance of integrating both static topographic parameters and dynamic flow mechanics when assessing co-seismic landslide hazards, particularly for predicting potential runout distances on gentle slopes where human settlements are often located. Our methodology provides a framework for improved landslide susceptibility assessment and disaster risk reduction in seismically active regions. Full article

In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article

Earthquakes and the secondary hazards they trigger, such as landslides, collapses, and debris flows, profoundly reshape the land surface and cause significant casualties, property damage and ecological disruption. This study collected 312 strong ground motion records from 19 seismic events in China, with magnitudes ranging from Ms5.2 to Ms8.0. Using the Newmark sliding block method and programming, permanent displacements for earthquake-induced landslides with varying yield accelerations were calculated. Two models (Model 1 and Model 2) for predicting permanent displacements of earthquake-induced landslides were developed through multiple regression analysis. Results show that the goodness of fit (R²) for the permanent displacement (logu) in Model 1 and Model 2 is 0.866 and 0.923, respectively. Model 2 incorporates higher-order terms of yield acceleration ratio (a_y/PGA), which effectively reduce nonlinearity in the residuals observed in Model 1 and enhance its accuracy. Finally, these models were compared with classical empirical models. Models 1 and 2, by calculating permanent displacement from ground motion data, provide critical insights into the mechanisms of earthquake-induced landslides, and play a key role in enhancing emergency response strategies for seismic geological hazards. Full article

This study presents the first direct cosmogenic ³⁶Cl-based chronology of landscape evolution and ground deformation in the Ionian Islands, focusing on the Thinia Valley in northern Kefalonia, western Greece. At the Zola site, exposure ages indicate that the eastern limb of the associated anticline has undergone intermittent deformation since at least 34 ka, with ongoing exhumation still occurring today. Variability in erosion rates suggests a complex deformation history, with lower-elevation samples exhuming faster than those at higher elevations. The findings highlight the role of progressive landslide activity rather than a single catastrophic failure. The compression-induced asymmetry of the Zola anticline, along with regional seismicity, appears to control slope instability. The exposure ages at the SK site reveal a surface that reached steady-state long before 20 ka, with a uniform erosion rate of 47.72 ± 0.82 m·Ma⁻¹, consistent with regional estimates. Additionally, a prehistoric earthquake—dated at 4.8 ± 0.14 ka—has been identified, with a planar surface exhumed in a single slip event. These findings emphasize the tectonic mobility of the region, with deformation processes persisting since the Middle Pleistocene. The results contribute to a broader understanding of fault-controlled slope instability and have direct implications for seismic hazard assessment in actively deforming terrains. Full article

Debris flows are rapid, destructive landslides that pose significant risks in mountainous regions. This study presents a novel algorithm to simulate debris flow dynamics, focusing on sediment transport from 0-order basins to depositional zones. The algorithm integrates the D8 flow direction method with an adjustable friction coefficient to enhance the accuracy of debris flow trajectory and deposition modeling. Its performance was evaluated on three real-world cases in the Otoishi River basin, affected by rainfall-induced debris flows in July 2017, and the Aso Bridge landslide triggered by the 2016 Kumamoto Earthquake. By utilizing diverse friction coefficients, the study effectively captured variations in debris flow behavior, transitioning from fluid-like to more viscous states. Simulation results demonstrated a precision of 88.9% in predicting debris flow paths and deposition areas, emphasizing the pivotal role of the friction coefficient in regulating mass movement dynamics. Additionally, Monte Carlo (MC) simulations enhanced the identification of critical slip surfaces within 0-order basins, increasing the accuracy of debris flow source detection. This research offers valuable insights into debris flow hazards and risk mitigation strategies. The algorithm’s proven effectiveness in simulating real-world scenarios highlights its potential for integration into disaster risk assessment and prevention frameworks. By providing a reliable tool for hazard identification and prediction, this study supports proactive disaster management and aligns with the goals of sustainable development in regions prone to debris flow disasters. Full article

Many landslides triggered by earthquakes have caused a countless loss of life and property, therefore, it is very important to predict landslide hazards accurately. In this work, regional seismic landslide data were obtained via a field survey, remote sensing interpretation, and data collection, and a multilevel physical and mechanical parameter system for seismic landslide hazard assessment was established; this system included a landslide inventory, loose accumulation layers, and geological units, enabling higher accuracy in the data. The Newmark displacement model with a modified correlation coefficient was used to assess the regional seismic landslide hazard in four scenarios (a = 0.1, 0.2, 0.3, 0.4) to study the influence of the landslide hazard at different peak ground accelerations. Moreover, the information value model was used to modify the calculated results to improve their accuracy in the assessment. By assessing the potential seismic landslide hazard in Shimian County in the upper reaches of the Yangtze River, the regional landslide distribution and pattern at different peak ground accelerations were obtained. The results show that with decreasing parameter accuracy in the system, the importance of the landslide inventory increases. When the peak ground acceleration is a = 0.3, which can be defined as a high hazard grade, in which the landslide area demonstrates a large-scale sharp increase, a devastating hazard threshold is reached. As the peak ground acceleration increases, the factor controlling landslides transforms from the landslide inventory to the slope, which reflects the reasonableness of the parameters in the system. The input parameters were regarded as important factors for efficiently increasing the accuracy of the results of the Newmark displacement model in the discussion. Full article

The coast is the most dynamic part of the Earth’s surface due to its strategic position at the interface of the land and the sea. It is, therefore, exposed to hazards and specific risks because of the geography as well as the geological and environmental characteristics of different countries. The coastal environment is essentially dynamic and evolving in time and space, marked by waves, tides, and seasons; moreover, it is subjected to many marine and continental processes (forcing). This succession of events significantly influences the frequency and severity of coastal hazards. The present paper aims at describing and characterizing the hazards and vulnerabilities on the Cameroonian coast. Cameroon possesses 400 km of coastline, which is exposed to various hazards. It is important to determine the probabilities of these hazards, the associated effects, and the related vulnerabilities. In this study, in this stable intraplate setting, the methodology used was diverse and combined techniques for the study of the shore and methods for the treatment of climatic data. Also, historical data were collected during field observations and from the CRED website for all the natural hazards recorded in Cameroon. In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. A critical grid combining a severity and frequency analysis was used to better understand these hazards and the coastal vulnerabilities of Cameroon. The results show that Cameroon’s coastal margins are subject to natural processes that cause shoreline changes, including inundation, erosion, and accretion. This study identified seven primary hazard types (earthquakes, volcanism, landslides, floods, erosion, sea level rise, and black tides) affecting the Cameroonian coastline, with the erosion rate exceeding 1.15 m/year at Cape Cameroon. Coastal populations are continuously threatened by these natural or man-induced hazards, and they are periodically subjected to catastrophic disasters such as floods and landslides, as experienced in Cameroon. In addition, despite the existence of the National Contingency Plan devised by the Directorate of Civil Protection, National Risk, and Climate Change Observatories, the implementation of disaster risk reduction and mitigation strategies is suboptimal. Full article

The steep northern slopes of Giona Mt in central continental Greece are the result of an E-W normal fault dipping 35–45° to the north, extending from the Mornos River in the west to the village of Gravia in the east. This fault creates a significant elevation difference of approximately 1500 m between the northern Giona footwall and the southern Iti hanging wall. The footwall comprises imbricated Mesozoic carbonates of the Parnassos unit, which exhibit large-scale drag folding near and parallel to the fault. The hanging wall comprises deformed sedimentary rocks of the Beotian unit and tectonic klippen of the Eastern Greece unit, forming a southward-tilted neotectonic block with subsidence near the Northern Giona Fault and uplift near the Ypati fault to the north. These two E-W faults represent younger structures disrupting the older NNW-trending tectonic framework. Fault scarps are observed all along the 14 km length of the Northern Giona fault accompanied by cataclastic zones, separating the carbonate formations of the Parnassos Unit from thick scree, slide blocks, boulders and olistholites. Inversion of fault-slip data has shown a mean slip vector of 45°, N004°E, which aligns with the current regional extensional deformation of the area, as confirmed by focal mechanism solutions. Based on the general asymmetry of the alpine units in the hanging wall, we interpret a listric fault geometry at depth using slip-line analysis and we forward modelled a disrupted fault-propagation fold using kinematic trishear algorithms, estimating a total displacement of 6500 m and a throw of approximately 2000 m. Seismic activity in the area of the Northern Giona Fault includes a magnitude 6.1 earthquake in 1852, which caused casualties, rockfalls and extensive damage, as well as a magnitude 5.1 event in 1983. The expected seismic magnitude is deterministically estimated between 6.2 and 6.7, depending on the potential westward continuation of the Northern Giona Fault beyond the Mornos River to the Northern Vardoussia saddle. The seismic hazard zone includes several villages located near the fault, particularly on the hanging wall, where intense landslide activity during seismic events could result in severe damage to regional infrastructure. The neotectonic development of the Northern Giona Fault highlights the importance of extending seismotectonic research into the mountainous regions of central Greece within the alpine formations, beyond the post-orogenic sedimentary basins. Full article

The present work explores the use of geophysical surveys as valuable tools for the study and sustainable management of landslides, with a particular focus on Ecuador. As an Andean country, Ecuador’s geomorphology and geology are dominated by volcano-sedimentary materials and processes, which confers a high susceptibility to landslides. In the last few years, a number of landslide events (such as those at La Josefina, Alausí, and Chunchi) have given rise to disasters with significant material damage and loss of life. Climatic events, affected by climate change, earthquakes, and human activity, are the main landslide triggers. Geophysical surveys, like seismic refraction, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR), are easy and low-cost techniques that provide valuable and critical subsurface data. They can help define the failure surface, delimit the mobilized materials, describe the internal structure, and identify the hydrological and geotechnical parameters that complement any direct survey (like boreholes and laboratory tests). As a result, they can be used in assessing landslide susceptibility and integrated into early warning systems, mapping, and zoning. Some case examples of large landslide events in Ecuador (historical and recent) are analyzed, showing how geophysical surveys can be a valuable tool to monitor landslides, mitigate their effects, and/or develop solutions. Combined or isolated geophysical techniques foster sustainable management, improve hazard characterization, help protect the most vulnerable regions, promote community awareness for greater safety and resilience against landslides, and support governmental actions and policies. Full article

The increasing frequency of earthquake events worldwide, particularly in South Korea, necessitates detailed seismic hazard assessments to mitigate the risks to urban infrastructure. This study addresses this pressing need by developing a comprehensive multi-hazard assessment framework specific to the Gyeonggi-do Province. By leveraging advanced geospatial computation techniques and geographic information systems, this study integrated geotechnical data, terrain information, and building inventories to evaluate seismic site effects, earthquake-induced landslide hazards, and structural vulnerability. This method uses geostatistical methods to construct geotechnical spatial grids that correlate site-specific seismic responses to potential hazards. The key findings revealed significant variations in seismic site responses owing to local subsurface characteristics, emphasizing the importance of site-specific seismic hazard maps for urban disaster preparedness. The framework’s effectiveness was validated by analyzing the 2017 Pohang earthquake, which demonstrated a strong correlation between predicted and observed damage. This study highlights the importance of ongoing seismic hazard assessment methodology development and advocates interdisciplinary collaboration to improve urban resilience, ultimately protecting communities from the impacts of future earthquakes. Full article