Search Results (316)

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

Rainfall infiltration is a key factor affecting the stability of the slope. To study the impact of rainfall on the instability mechanism and stability of slopes, this paper employs numerical simulation to establish a rainfall infiltration slope model and conducts a saturated–unsaturated slope flow and solid coupling numerical analysis. By combining the strength reduction method with the calculation of slope stability under rainfall infiltration, the safety factor of the slope is obtained. A comprehensive analysis is conducted from the perspectives of the seepage field, displacement field and other factors to examine the impact of heavy rainfall patterns and rainfall intensities on the instability mechanism and stability of the slope. The results indicate that heavy rainfall causes the transient saturation zone within the landslide body to continuously move upward, forming a continuous sliding surface inside the slope, which may lead to instability and sliding of the soil in the upper part of the slope toe. The heavy rainfall patterns significantly affect the temporal and spatial evolution of pore water pressure, displacement and safety factors of the slope. Pore water pressure and displacement show a positive correlation with the rainfall intensity at various times during heavy rainfall events. The pre-peak rainfall pattern causes the largest decrease in the safety factor of the slope, and the slope failure occurs earlier, which is the most detrimental to the stability of the slope. The rainfall intensity is inversely proportional to the safety factor. As the rainfall intensity increases, the decrease in the slope’s safety factor becomes more significant, and the time required for slope instability is also shortened. The results of this study provide a scientific basis for analyzing rainfall-induced slope instability and failure. Full article

Reliable rainfall thresholds are critical for effective early warning and mitigating the risks of rainfall-induced landslides. Traditional statistical models have limitations in multi-variable modeling, while machine learning models face interpretability challenges. Explainable machine learning methods can address these challenges, but they are rarely applied to rainfall threshold modeling. In this study, we compared the performance of an empirical statistical model and machine learning models for predicting rainfall-induced landslides in Italy. Based on the optimal model, we visualized refined rainfall thresholds at three probability levels and employed SHAP (Shapley Additive Explanations) to enhance model explainability by quantifying the contribution of each input variable to the predictions. The results demonstrated that the XGBoost model achieved a good performance (AUC = 0.917 ± 0.026) with well-balanced sensitivity (0.792 ± 0.075) and specificity (0.812 ± 0.033) in landslide susceptibility modeling. Hydrological factors, particularly total rainfall, were identified as the dominant triggering mechanisms, with SHAP analysis confirming their substantially greater contribution compared to environmental factors in rainfall threshold modeling. The developed visualized threshold maps revealed distinct spatial variations in landslide-triggering rainfall thresholds across Italy, characterized by lower thresholds in gentle slope areas with moderate annual precipitation and higher thresholds in steep slope and mid-to-low-elevation regions, while these regional differences decreased under high-probability scenarios. This study offered a modeling approach for regional rainfall threshold assessment by integrating multi-variable modeling with explainable methods, contributing to the development of landslide early warning systems. Full article

Under the background of global climate change, shallow landslide clusters induced by extreme rainfall are occurring with increasing frequency, causing severe casualties and economic losses. To address this challenge, this study proposes an integrated approach to support both emergency response and long-term mitigation for rainfall-induced shallow landslides. The workflow includes (1) rapid landslide detection based on time-series image fusion and threshold segmentation on the Google Earth Engine (GEE) platform; (2) numerical simulation of landslide runout using the R.avaflow model; (3) landslide susceptibility assessment based on event-driven inventories and machine learning; and (4) delineation of high-risk slopes by integrating simulation outputs, susceptibility results, and exposed elements. Applied to Qugaona Township in Zhouqu County, Bailong River Basin, the framework identified 747 landslides. The R.avaflow simulations captured the spatial extent and depositional features of landslides, assisting post-disaster operations. The Gradient Boosting-based susceptibility model achieved an accuracy of 0.870, with 8.0% of the area classified as highly susceptible. In Cangan Village, high-risk slopes were delineated, with 31.08%, 17.85%, and 22.42% of slopes potentially affecting buildings, farmland, and roads, respectively. The study recommends engineering interventions for these areas. Compared with traditional methods, this approach demonstrates greater applicability and provides a more comprehensive basis for managing rainfall-induced landslide hazards. Full article

The soil water characteristic curve (SWCC) is a key foundation in unsaturated soil mechanics describing the relationship between matric suction and water content, which is crucial for studies on effective stress, permeability coefficients, and other soil properties. In natural environments, colluvial and residual soils typically exhibit high pore heterogeneity, and previous studies have shown that the SWCC is closely related to the distribution of pore sizes. The SWCC of soils may display either a unimodal or bimodal distribution, leading to different hydraulic behaviors. Past unsaturated slope stability analyses have used the unimodal SWCC model, but this assumption may result in evaluation errors, affecting the accuracy of seepage and slope stability analyses. This study proposes a novel bimodal hydro-mechanical coupling model to investigate the influence of bimodal SWCC representations on rainfall-induced seepage behavior and stability of unsaturated slopes. By fitting the unimodal and bimodal SWCCs with experimental data, the results show that the bimodal model provides a higher degree of fit and smaller errors, offering a more accurate description of the relationship between matric suction and effective saturation, thus improving the accuracy of soil hydraulic property assessment. Furthermore, the study established a hypothetical slope model and used field data of landslides to simulate the collapse of Babaoliao in Chiayi County, Taiwan. The results show that the bimodal model predicts slope instability 1 to 3 h earlier than the unimodal model, with the rate of change in the safety factor being about 16.6% to 25.1% higher. The research results indicate the superiority of the bimodal model in soils with dual-porosity structures. The bimodal model can improve the accuracy and reliability of slope stability assessments. Full article

This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau. Full article

Shallow landslides are one of the main geological hazards that occur during heavy rainfall in Yuexi County every year, posing potential risks to the personal and property safety of local residents. A rainfall-induced shallow landslide named Baishizu No. 15 landslide in Yuexi Country was taken as a case study. Based on the field geological investigation, combined with physical and mechanical experiments in laboratory as well as numerical simulation, the failure mechanism induced by rainfall infiltration was studied, and the movement process after landslide failure was inverted. The results show that the pore-water pressure within 2 m of the landslide body increases significantly and the factory of safety (F_s) has a good corresponding relationship with rainfall, which decreased to 0.978 after the heavy rainstorm on July 5 and July 6 in 2020. The maximum shear strain and displacement are concentrated at the foot and front edge of the landslide, which indicates a “traction type” failure mode of the Baishizu No. 15 landslide. In addition, the maximum displacement during landslide instability is about 0.5 m. The residual strength of soils collected from the soil–rock interface shows significant rate-strengthening, which ensures that the Baishizu No. 15 landslide will not exhibit high-speed and long runout movement. The rate-dependent friction coefficient of sliding surface was considered to simulate the movement process of the Baishizu No. 15 landslide by using PFC^2D. The simulation results show that the movement velocity exhibited obvious oscillatory characteristics. After the movement stopped, the landslide formed a slip cliff at the rear edge and deposited as far as the platform at the front of the slope foot but did not block the road ahead. The final deposition state is basically consistent with the on-site investigation. The research results of this paper can provide valuable references for the disaster prevention, mitigation, and risk assessment of shallow landslides on residual soil slopes in the Dabie mountainous region. Full article

Landslides pose significant risks to human life and infrastructure, driven by a complex interplay of geological and hydrological factors. This study investigates the ongoing slope instability affecting the village of Borrano, in Central Italy, where large-scale landslides are triggered or reactivated by extreme rainfall and seismic activity. A multidisciplinary approach was employed, integrating traditional geological surveys, direct investigations, and advanced geophysical techniques—including electrical resistivity tomography (ERT) and seismic refraction tomography (SRT)—to characterize subsurface structures. Additionally, Sentinel-1 interferometric synthetic aperture radar (InSAR) was employed to parametrize the deformation rates induced by the landslide. The results reveal a complex geological framework dominated by the Teramo Flysch, where weak clayey facies and structurally controlled dip-slopes predispose the area to gravitational instability. ERT and SRT identified resistivity and velocity contrasts associated with shallow and depth sliding surfaces. At the same time, satellite-based synthetic aperture radar (SAR) data confirmed persistent slow movements, with vertical displacement rates between −10 and −24 mm/year. These findings underscore the importance of lithological heterogeneity and structural settings in the evolution of landslides. The integrated geophysical and remote sensing approach enhances the understanding of slope dynamics. It can be used to cross-check interpretations, capture displacement trends, characterize the internal structure of unstable slopes, and resolve the limitations of each method. This synergy provides a more comprehensive assessment of complex slope instability, offering valuable insights for hazard mitigation strategies in landslide-prone areas. Full article

Landslides in Ecuador are one of the most common deadly events in natural hazards, such as the one on 26 March 2023. A large-scale landslide occurred in Alausí, Chimborazo province, causing 65 fatalities and 10 people to disappear, significant infrastructural damage, and the destruction of six neighborhoods. This study presents a detailed case analysis of the anthropogenic factors that could have contributed to the instability of the affected area. Field investigations and a review of historical, geological, and social information are the basis for analyzing the complex interactions between natural and human-induced conditions. Key anthropogenic contributors identified include unplanned urban expansion, ineffective drainage systems, deforestation, road construction without adequate geotechnical support, and changes in land use, particularly agricultural irrigation and wastewater disposal. These factors increased the area’s susceptibility to slope failure, which, combined with intense rainfall and past seismic activity, could have caused the rupture process’s acceleration. The study also emphasizes integrating geological, hydrological, and urban planning assessments to mitigate landslide risks in geologically sensitive regions such as Alausí canton. The findings conclude that human activity could be an acceleration factor in natural processes, and the pressure of urbanization amplifies the consequences. This research underscores the importance of sustainable land management, improved drainage infrastructure, and land-use planning in hazard-prone areas. The lessons learned from Alausí can inform risk reduction strategies across other mountainous and densely populated regions worldwide, like the Andean countries, which have similar social and environmental conditions to Ecuador. Full article

Precipitation typically leads to the accumulation of soil moisture, which causes slope instability and triggers landslides. However, due to the lag nature of this process, landslides usually do not occur on the day of heavy rainfall. Therefore, it is essential to incorporate antecedent effective precipitation as a factor in landslide prediction models that allow for the creation of more comprehensive landslide susceptibility maps. In this study, six machine learning models are compared, with antecedent effective precipitation included as a conditioning factor for model training. The optimal model is selected to simulate landslide susceptibility maps under four return periods (5, 10, 20, and 50 years). Additionally, the mean decreases in the Gini and SHAP values are employed to identify the most significant factors contributing to landslides. The results indicate the following: (1) Effective antecedent precipitation is the most influential factor in landslide occurrence, ranging from one to two times higher than other factors. (2) Most meteorological stations in the study area show antecedent effective precipitation that follows a lognormal distribution, mainly in coastal areas, with a secondary fit to the general extreme value distribution. The spatial distribution of antecedent effective precipitation is more prominent in the coastal and western mountainous regions, with lower values that then increase with longer return periods in central areas. (3) The XGBoost model achieves the best performance, with an area under the curve of 0.96 and an accuracy of 89.02%. (4) The landslide susceptibility maps for the four return periods reveal three high-risk zones: the southern coastal mountains, the western Zhejiang mountains, and the areas surrounding the hilly region of Shaoxing to Taizhou in central Zhejiang. This study provides dynamic decision-making support for the prevention and control of rainstorm-induced landslide risks. Full article

Rapid acquisition of 3D reconstruction models of landslides is crucial for post-disaster emergency response and rescue operations. This study explores the application potential of Neural Radiance Fields (NeRF) technology for rapid post-disaster site modeling and performs a comparative analysis with traditional photogrammetry methods. Taking a landslide induced by heavy rainfall in Sasebo City, Japan, as a case study, this research utilizes drone-acquired video imagery data and employs two different 3D reconstruction techniques to create digital models of the landslide area. Visual realism and point cloud detail were compared. The results indicate that the high-capacity NeRF model (NeRF 24G) approaches or even surpasses traditional photogrammetry in visual realism under certain scenarios; however, the generated point clouds are inferior in terms of detail compared to those produced by traditional photogrammetry. Nevertheless, NeRF significantly reduces the modeling time. NeRF 6G can generate a point cloud of engineering-useful accuracy in only 45 min, providing a 3D overview of the disaster site to support emergency response efforts. In the future, integrating the advantages of both methods could enable rapid and precise post-disaster 3D reconstruction. Full article

Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies. Full article

Geotechnical slope failures—often precursors to catastrophic landslides and collapses—pose significant risks to mining operations and regional socioeconomic stability. Focusing on the Jiangte Xikeng lithium open-pit mine, this study integrates field reconnaissance, laboratory testing, and multi-physics numerical modeling to elucidate the mechanisms governing slope stability. Geological surveys and core analyses reveal a predominantly granite lithostratigraphy, bisected by two principal fault systems: the NE-striking F01 and the NNE-oriented F02. Advanced three-dimensional finite element simulations—accounting for gravitational loading, hydrogeological processes, dynamic blasting stresses, and extreme rainfall events—demonstrate that strain localizes at slope crests, with maximum displacements reaching 195.7 mm under blasting conditions. They indicate that differentiated slope angles of 42° for intact granite versus 27° for fractured zones are required for optimal stability, and that the integration of fault-controlled instability criteria, a coupled hydro-mechanical-blasting interaction model, and zonal design protocols for heterogeneous rock masses provides both operational guidelines for hazard mitigation and theoretical insights into excavation-induced slope deformations in complex metallogenic environments. Full article

Rainfall-induced landslide mitigation remains a critical research focus in geotechnical engineering, particularly for safeguarding buildings and infrastructure in unstable terrain. This study investigates the stabilizing performance of slopes reinforced with negative Poisson’s ratio (NPR) anchor cables under rainfall conditions through physical model tests. A scaled geological model of a heavily weathered rock slope is constructed using similarity-based materials, building a comprehensive experimental setup that integrates an artificial rainfall simulation system, a model-scale NPR anchor cable reinforcement system, and a multi-parameter data monitoring system. Real-time measurements of NPR anchor cable axial forces and slope internal stresses were obtained during simulated rainfall events. The experimental results reveal distinct response times and force distributions between upper and lower NPR anchor cables in reaction to rainfall-induced slope deformation, reflecting the temporal and spatial evolution of the slope’s internal sliding surface—including its generation, expansion, and full penetration. Monitoring data on volumetric water content, earth pressure, and pore water pressure within the slope further elucidate the evolution of effective stress in the rock–soil mass under saturation. Comparative analysis of NPR cable forces and effective stress trends demonstrates that NPR anchor cables provide adaptive stress compensation, dynamically counteracting internal stress redistribution in the slope. In addition, the structural characteristics of NPR anchor cables can effectively absorb the energy released by landslides, mitigating large deformations that could endanger adjacent buildings. These findings highlight the potential of NPR anchor cables as an innovative reinforcement strategy for rainfall-triggered landslide prevention, offering practical solutions for slope stabilization near buildings and enhancing the resilience of building-related infrastructure. Full article

This study examines the stability of the Huangyukou landslide in Yanqing District, Beijing, under varying rainfall conditions, focusing on the effects of rainfall infiltration and surface runoff on slope stability. Using a combination of field surveys, geophysical methods, drone photogrammetry, and laboratory testing, a high-precision 2D and 3D numerical model was developed. A hydrological-soil-structure coupling model was employed to simulate rainfall-induced infiltration and runoff processes, revealing that increased saturation and pore water pressure significantly reduce shear strength, enhancing the risk of slope failure. Stability analysis, using a reduction factor method, yielded stability coefficients of 1.06 and 1.04 for 20-year and 100-year return period rainfall scenarios, respectively. The results highlight the critical role of rainfall in destabilizing the upper layers of dolomite and shale, with significant deformation observed in the middle and rear slope sections. This research provides a comprehensive framework for assessing landslide risk under extreme rainfall events, offering practical implications for risk mitigation in similar geological contexts. Full article