Search Results (580)

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

Progressive rock slope destabilization poses significant geohazard risks, necessitating advanced monitoring frameworks to detect precursory failure signals. This study presents a comprehensive time-dependent evaluation of the displacement probability (CTEDP) model, which integrates GNSS-derived spatiotemporal data with multi-parameter reliability indices to enhance landslide risk assessment. Five monitoring points on a destabilizing rock slope were analyzed from mid-November 2024 to early January 2025 using kinematic metrics (velocity, acceleration, and jerk), statistical measures (e.g., moving averages), and reliability indices (RI₀, RI₁, RI₂, and RI_combined). Point 1 exhibited the most critical behavior, with a cumulative displacement of ~60 mm, peak velocities of 34.5 mm/day, and accelerations up to 1.15 mm/day². The CTEDP for active points converged to 0.56–0.61, indicating sustained high risk. The 90th percentile displacement threshold was 58.48 mm for Point 1. Sensitivity analysis demonstrated that the GNSS-derived reliability indices dominated the RI_combined variance (r = 0.999, explaining 99.8% of variance). The first- and second-order reliability indices (RI₁, RI₂) at Point 1 exceeded the 60-index threshold, indicating a transition to Class B (“Low Risk—Trend Surveillance Required”) status, while other points showed coherent deformation of 37–45 mm. Results underscore the framework’s ability to integrate spatiotemporal displacement, kinematic precursors, and statistical variability for early-warning systems. This approach bridges gaps in landslide prediction by accounting for spatial heterogeneity and nonlinear geomechanical responses. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

This study investigates rainstorm-induced highway subgrade slope collapses in the coastal areas of Southeast China. By integrating the seepage–stress coupled finite element method with the strength reduction method, we simulate the entire process of seepage, deformation, and slope collapse under rainstorm conditions, analyzing the variation in the stability factor. The key findings are as follows: (1) During rainstorms, water infiltration increases soil saturation and pore water pressure, while reducing matrix suction and soil shear strength, leading to soil softening. (2) The toe of the subgrade slope first undergoes plastic deformation under rainstorms, which develops upward, and finally the plastic zone connects completely, causing collapse. The simulated landslide surface is consistent with the actual one, revealing the collapse mechanism of the subgrade slope. Additionally, the simulated displacement at the slope toe when the plastic zone connects provides valuable insights for setting warning thresholds in landslide monitoring. (3) The stability factor of the subgrade slope in the case study decreased from 1.24 before the rainstorm to 0.985 after the rainstorm, indicating a transition from a stable state to an unstable state. (4) Parameter analysis shows that heavy downpour or downpour will cause the case subgrade slope to enter an unstable state. The longer the rainfall duration, the lower the stability factor. Analysis of soil parameters indicates that strength parameters, internal friction angle, and effective cohesion exert a significant influence on slope stability, whereas deformation parameters, elastic modulus, and Poisson’s ratio have a negligible effect. Slope collapse can be timely forecasted by predicting the stability factor. Full article

This review aims to provide a complete and comprehensive state of the art of the SCOSSA computer code, which is a one-dimensional nonlinear computer code used for the analysis of seismic site response and soil instability. Indeed, among the effects of earthquakes, the activation of landslides and liquefaction constitute two of the predominant causes of vulnerability in the physical and built environment. The SCOSSA computer code (Seismic Code for Stick–Slip Analysis) was initially developed to evaluate the permanent displacements of simplified slopes using a coupled model, and introduced several improvements with respect to the past, namely, the formulation for solving the dynamic equilibrium equations incorporates the capability for automated detection of the critical sliding surface; an up-to-date constitutive model to represent hysteretic material behavior and a stable iterative algorithm to support the solution of the system in terms of kinematic variables. To address liquefaction-induced failure, a simplified pore water pressure generation model was subsequently developed and integrated into the code, coupled with one-dimensional consolidation theory. This review retraces the main features, developments, and applications of the computer code from the origin to the present version. Full article

Increasingly frequent intense rainfall is increasing landslide occurrence and risk. In southern China in particular, steep slopes and thin residual soils produce frequent landslide events with pronounced spatial heterogeneity. Therefore, displacement prediction methods that function across sites and deformation regimes in similar settings are essential for early warning. Most existing approaches adopt a multistage pipeline that decomposes, predicts, and recombines, often leading to complex architectures with weak cross-domain transfer and limited adaptability. To address these limitations, we present CRAFormer, a causal role-aware Transformer guided by a dynamic-lag Bayesian network-style causal graph learned from historical observations. In our system, the discovered directed acyclic graph (DAG) partitions drivers into five causal roles and induces role-specific, non-anticipative masks for lightweight branch encoders, while a context-aware Top-2 gate sparsely fuses the branch outputs, yielding sample-wise attributions. To safely exploit exogenous rainfall forecasts, next-day rainfall is entered exclusively through an ICS tail with a leakage-free block mask, a non-negative readout, and a rainfall monotonicity regularizer. In this study, we curate two long-term GNSS datasets from Guangxi (LaMenTun and BaYiTun) that capture slow creep and step-like motions during extreme rainfall. Under identical inputs and a unified protocol, CRAFormer reduces the MAE and RMSE by 59–79% across stations relative to the strongest baseline, and it lowers magnitude errors near turning points and step events, demonstrating robust performance for two contrasting landslides within a shared regional setting. Ablations confirm the contributions of the DBN-style causal masks, the leakage-free ICS tail, and the monotonicity prior. These results highlight a practical path from causal discovery to forecast-compatible neural predictors for rainfall-induced landslides. Full article

Natural disasters, particularly floods and landslides, can cause severe losses; however, their impacts can be significantly mitigated through proactive planning. In August 2021, a devastating flood in northern Türkiye resulted in major damage, including the displacement of logs from the Ayancık Forest Management Directorate’s depot, which exacerbated the disaster’s effects. This study aims to identify the most suitable location for a new forest depot in Ayancık, considering disaster risk, logistical needs, and environmental factors. A hybrid geospatial approach was employed by integrating Logistic Regression (LR)-based landslide susceptibility modeling and the Analytic Hierarchy Process (AHP). Key conditioning factors such as altitude, slope, aspect, lithology, land cover, plan and profile curvature, topographic wetness index (TWI), distance to drainage networks, roads, and faults were used to produce the LSM. The AHP weights of the factors used in selecting a suitable depot location were determined based on expert opinions. The integration of physical, logistical, and risk-based parameters allowed for a spatial prioritization of suitable areas. Results indicate that approximately 10.69% of the study area is classified as class 1 (very high suitability), 16.59% as class 2 (high), 20.71% as class 3 (moderate), 23.34% as class 4 (low), and 28.67% as class 5 (very low), corresponding to 27.28% of the area in classes 1–2 and 52.01% in classes 4–5. These results indicate that the study area is predominantly characterized by medium-low suitability conditions. Notably, these areas show significantly lower flood and landslide susceptibility compared to the current depot sites. By aligning forest infrastructure planning with disaster resilience principles, this study offers a replicable model for sustainable forest depot site selection. The findings provide valuable guidance for forest managers and policymakers to enhance the safety, functionality, and long-term viability of forestry operations in hazard-prone regions. Full article

Monitoring landslide displacement and anti-slide pile damage is critical for assessing the stability of progressive loess landslides. To address the challenge of capturing precursor information for loess landslide instability under anti-slide pile reinforcement, this study systematically investigates the damage evolution process of slides (through their “slide-stability-reslide” cycles) and anti-slide piles under acoustic emission (AE) monitoring. Cyclic loading tests were employed to simulate the movement of progressive loess landslides. Based on the core causal logic that “slide displacement induces pile damage, damage generates AE signals, and signals invert displacement status”, a laboratory-scale physical model was designed to simultaneously monitor slide displacement, pile stress, and AE signals. The research results indicate that the dominant frequency and amplitude of AE signals are significantly correlated with slide displacement: with cyclic loading, both the dominant frequency and amplitude exhibit a “low → high → low” characteristic, corresponding to “low/medium-frequency low-amplitude”, “medium/high-frequency medium-high-amplitude” and “low-frequency medium-high-amplitude” signals in the three stages of slide deformation, respectively. The Kaiser and Felicity effects effectively monitor pile damage, and the decrease in Felicity ratio serves as a precursor for landslide early warning. Research results can provide a new methodological framework for early warning systems in pile-reinforced loess landslides. Full article

In many mountainous areas of China, frequent geological disasters pose a serious threat to human life and property. The Luding “9.5” earthquake triggered a large number of landslide disasters, causing serious loss of life and property. Therefore, it is extremely urgent to carry out research on the stability analysis and treatment methods of landslides in the Luding area. In this paper, the Caiyangba landslide in Yanzigou Town, Luding County, is taken as the research object. The slope model is constructed by Midas to study the stability development law of Caiyangba landslide under different rainfall conditions and seismic conditions, and to explore the feasibility of the “anchor lattice treatment method”. The results show that the “anchor lattice treatment method” can effectively improve the stability of the slope under rainfall conditions. The improvement effect of slope stability decreases with the increase in rainfall duration and rainfall. The development law of the slope stability coefficient with rainfall duration in WMG (the working condition of not adopting the “anchor lattice treatment method” is referred to as WMG) and MG (the working condition of adopting the “anchor lattice treatment method” is referred to as MG) conditions conform to the development law of exponential function, and the expression of instantaneous change rate of slope stability coefficient is derived. The above function can also well explain the development law of X-direction displacement and Y-direction displacement of SP (school: monitoring point) and RP (road: monitoring point); the development law of the instantaneous change rate of displacement. Under the influence of ground motion, the improvement effect of the “anchor lattice treatment method” on the slope stability coefficient is limited, but the improvement effect of slope stability increases with the increase in seismic intensity. The slope stability coefficient and the displacement of SP and RP show obvious fluctuation with time, and the fluctuation law is similar to that of ground motion records. It is recommended to add a gravity-retaining wall at the foot of the slope. The teaching building reduces the number of floors and increases the number of pile foundations. Roads should restrict the passage of heavy vehicles, such as cars and strictly stacked items. The above results can provide a theoretical reference for the sustainable treatment and sustainable development of landslides in the Luding area. Full article

Stability analysis of landslide areas represents a critical issue in many countries, as landslides can cause large material damage and are a threat to the health and life of inhabitants. This article is aimed at the stability analysis of a built-up locality using a combination of traditional inclinometry with observations carried out using TDR technology (Time Domain Reflectometry) for displacement and groundwater level monitoring. Considering the geological conditions of the site and the occurrence of an old stabilized landslide, groundwater is the main trigger for possible slope deformations. The evaluation of the stability, based on the survey and monitoring outputs, was made using the Finite Element Method. The loss of stability was predicted for a certain uplift of groundwater level and seismic loading, which was lower than normative requirements. The presented case study demonstrates the need for an exhaustive and coordinated survey, as well as the importance of monitoring results and integrated analysis. This careful combination of activities enables us to understand the behavior of the landslide, to evaluate the stability potential of the slope, and to design effective protective measures. Full article

This study introduces a Hybrid Interferometric Approach (HIA) tailored for the detection, mapping, and measurement of landslides using Sentinel-1 satellite data. The HIA is specifically designed to identify ground displacements that exceed the detection thresholds of the European Ground Motion Service (EGMS), offering an enhanced capacity for monitoring faster-moving landslides. The methodology integrates multi-baseline interferometric analysis, utilizing backscattered signals from both point-like and distributed radar targets at full spatial resolution. The approach utilizes ten interferometric datasets acquired between 2017 and 2021 from both ascending and descending orbits. Each annual dataset is restricted to a six-month observation window to reduce temporal decorrelation effects. The HIA was implemented in a landslide-prone sector of the Dolomites, a UNESCO World Heritage Site located in the Eastern Italian Alps. Comparative evaluation against EGMS ground motion products demonstrates that the HIA significantly broadens the range of detectable slope instabilities, thus providing a valuable supplement to existing ground motion monitoring services and contributing meaningfully to landslide hazard assessment and risk reduction efforts. Full article

To realistically simulate the entire slip-surface process from crack initiation to run-out, we couple the simplified Bishop method (LEM) with zero-thickness cohesive elements (CZM): LEM first pinpoints the critical slip circle, then CZM tracks interface opening, progressive damage, and sliding along that exact surface. Benchmarked against ACADS EX11, the framework reproduces the classical factor of safety while delivering the post-failure displacements, energy dissipation, and crack paths that LEM or traditional FEM cannot capture, offering a practical tool for landslide-prone slope design. Full article

Introduction and Problem Statement: Landslides represent a significant geological hazard worldwide. One of the primary triggers for these landslides is rainfall, which is becoming more intense as a result of climate change. The available literature has produced extensive research. However, this largely overlooks the use of mixed methodologies. Furthermore, a comprehensive review combining empirical, physically based, deterministic, and phenomenological models is still rare. Objective and Method: This study (Part I of a two-part review) addresses this gap by employing a mixed review that integrates quantitative scientometric analysis with a qualitative systematic review. The primary objective of Part I is to deliver a critical assessment, focusing on empirical and physically based causative threshold models. Main Results and Validation: Macroscopically, our analysis reveals that antecedent rainfall is a more robust indicator than classical intensity–duration (I-D) thresholds, though the latter remains widely used due to its simplicity. Physically based models provide a critical bridge when geotechnical data is scarce, correlating rainfall with internal slope responses like displacement. At a microscopic level, hybrid artificial intelligence (AI) models consistently demonstrate superior predictive accuracy by capturing complex, nonlinear relationships missed by simpler models. These findings are validated through a systematic evaluation of performance metrics across the reviewed literature. Main Conclusions and Significance: We conclude that while empirical thresholds offer operational simplicity, the future of accurate prediction lies in sophisticated hybrid AI models trained on extensive monitoring data. This review synthesizes fragmented knowledge into a unified framework, providing a clear roadmap for model selection. Full article

Landslide disasters pose severe threats to mountainous regions, where accurate monitoring and scientific prediction are crucial for early warning and risk mitigation. This study addresses this challenge by focusing on the Outang Landslide, a representative large-scale bank slope in the Three Gorges Reservoir area known for its significant deformation responses to rainfall and reservoir-level fluctuations. The landslide’s behavior, characterized by notable hysteresis and nonlinear trends, poses a significant challenge to accurate prediction. To address this, we derived high-precision time-series deformation data by applying atmosphere-corrected Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to Sentinel-1A imagery, with validation from GNSS measurements. A systematic analysis was then conducted to uncover the correlation, hysteresis, and spatial heterogeneity between landslide deformation and key influencing variables (rainfall, water level, temperature). Furthermore, we proposed a Spatio-Temporal Enhanced Convolutional Neural Network (STE-CNN), which innovatively converts influencing variables into grayscale images to enhance spatial feature extraction, thereby improving prediction accuracy. The results indicate that: (1) From June 2022 to March 2024, the landslide showed an overall downward displacement trend, with maximum settlement and uplift rates of −49.34 mm/a and 21.77 mm/a, respectively; (2) Deformation exhibited significant correlation, hysteresis, and spatial variability with environmental factors, with dominant variables shifting across seasons—leading to intensified movement in flood seasons and relative stability in dry seasons; (3) The improved STE-CNN outperforms typical prediction models in forecasting landslide deformation.This study presents an integrated methodology that combines InSAR monitoring, multi-factor mechanistic analysis, and deep learning, offering a reliable solution for landslide early warning and risk management. Full article

Extensive high and steep open-pit slopes in gneiss are distributed in cold regions at high altitudes or high latitudes of China, such as Qinghai, Tibet, and Xinjiang, posing significant hazards to mine safety. Several recent slope failure incidents highlight the urgent need to study the failure modes and mechanisms of gneiss open-pit slopes in these cold regions. This study focuses on the 14 September 2023 landslide at the Jinbao Mine in Xinjiang. Initially, field investigation and displacement monitoring were employed to analyze its failure characteristics and mode. Subsequently, utilizing mechanical parameters of the gneissic foliation and the rock mass obtained under various conditions, discrete element numerical modeling was conducted to study the failure mechanisms. The results indicate that the landslide was a typical bedding failure characterized by an upper bedding-controlled sliding zone, combined with buckling and crushing of the slope toe. Under the long-term combined effects of rainfall, freeze–thaw cycles and blasting, the shear strength of the gneissic foliation decreased. This reduction led to a decrease in the anti-sliding force and an increase in the sliding force within the upper bedding-controlled sliding zone. Consequently, the load transferred to the rock mass at the slope toe progressively increased. Under prolonged compression, the toe rock mass experienced bending, which intensified over time. Coupled with the strength reduction caused by the repeated action of rainfall, freeze–thaw cycles and blasting, the toe rock mass gradually fractured and ultimately failed in a buckling mode. This led to the loss of support for the upper mass, which then subsided along the foliation, precipitating the landslide’s overall instability. Full article