Search Results (791)

In landslide susceptibility modeling, research has predominantly focused on predicting landslides by identifying predisposing factors, often using inventories primarily based on the highest points of landslide crowns. However, a significant challenge arises when the transported mass impacts human activities directly, typically occurring in the deposition areas of these phenomena. Therefore, identifying the terrain characteristics that facilitate the transport and deposition of displaced material in affected areas is equally crucial. This study aimed to evaluate the predictive capability of identifying where displaced material might be deposited by using different inventories of specific parts of a landslide, including the source area, intermediate area, and deposition area. A sample segmentation was conducted that included inventories of these distinct parts of the landslide in the hydrographic basin of Lake Ilopango, which experienced debris flows and debris floods triggered by heavy rainfall from Hurricane Ida in November 2009. Given the extensive variables extracted for this evaluation (20 variables), the Induced Smoothed (IS) version of the Least Absolute Shrinkage and Selection Operator (LASSO) methodology was employed to determine the significance of each variable within the datasets. Additionally, the Multivariate Adaptive Regression Splines (MARS) algorithm was used for modeling. Our findings revealed that models developed using the deposition area dataset were more effective compared with those based on the source area dataset. Furthermore, the accuracy of models using deposition area data surpassed that of that using data from both the source and intermediate areas. Full article

Frequent agricultural irrigation events continuously raise the groundwater table on loess platforms, triggering numerous loess landslides and significantly contributing to soil erosion in the Chinese Loess Plateau. The movement of irrigation water within the surficial loess layer is crucial for comprehending the mechanisms of moisture penetration into thick layers. To investigate the infiltration and evaporation processes of irrigation water, a large undisturbed soil column with a 60 cm inner diameter and 100 cm height was extracted from the surficial loess layer. An irrigation simulation event was executed on the undisturbed soil column and the ponding infiltration and subsequent evaporation processes were systematically monitored. A ruler placed above the soil column recorded the ponding height during irrigation. Moisture probes and tensiometers were installed at five depths to monitor the temporal variations in volumetric water content (VWC) and matric suction. Additionally, an evaporation gauge and an automatic weighing balance measured the potential and actual evaporation. The results revealed that the initially high infiltration rate rapidly decreased to a stable value slightly below the saturated hydraulic conductivity (K_s). A fitted Mezencev model successfully replicated the ponding infiltration process with a high correlation coefficient of 0.995. The monitored VWC of the surficial 15 cm-thick loess approached a saturated state upon the advancing of the wetting front, while the matric suction sharply decreased from an initial high value of 65 kPa to nearly 0 kPa. The monitored evaporation process of the soil column was divided into an initial constant rate stage and a subsequent decreasing rate stage. During the constant rate stage, the actual evaporation closely matched or slightly exceeded the potential evaporation rate. In the decreasing rate stage, the actual evaporation rate fell below the potential evaporation rate. The critical VWC ranged from 26% to 28%, with the corresponding matric suction recovering to approximately 25 kPa as the evaporation process transitioned between stages. The complete evaporation process was effectively modeled using a fitted Rose model with a high correlation coefficient (R² = 0.971). These findings provide valuable insights into predicting water infiltration and evaporation capacities in loess layers, thereby enhancing the understanding of water movement within thick loess deposits and the processes driving soil erosion. 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

Wildfires pose a significant threat to the entire ecosystem. The impacts of these wildfires can potentially disrupt biodiversity and ecological stability on a large scale. Wildfires may alter the physical and chemical properties of burned soil, such as particle size, loss of organic matter and infiltration capacity. These alterations can lead to increased vulnerability to geohazards such as landslides, mudflows and debris flows, where soil erosion and sediment transport play a crucial role. The present study investigates the impact of wildfire on soil erosion by conducting a series of laboratory experiments. The soil samples were burned using two different methods: using firewood for different burning durations and using a muffle furnace at an accurately controlled temperature within $400{}^{\circ }$C∼$1000{}^{\circ }$C. The burned soils were subsequently subjected to surface erosion by utilizing the constant head method to create a steady water flow to induce the erosion. In addition, empirically based theoretical models are explored to further assess the experimental results. The experimental results reveal a loss of organic matter in the burned soils that ranged from approximately 2% to 10% as the burning temperature rose from $400{}^{\circ }$C to $1000{}^{\circ }$C. The pattern of the grain size distribution shifted to a finer texture in the burned soil. There was also a considerable increase in soil erosion in burned soils, especially at a higher burn severity, where the erosion rate increased by more than five times. The empirical predictions are overall consistent with the experimental results and offer reasonable calibration of relevant soil erosion parameters. These findings demonstrate the importance of post-fire erosion in understanding and mitigating the long-term effects of wildfires on geo-environmental systems. Full article

This study systematically analyzed research trends and hot issues in the field of geological hazard prediction using bibliometric analysis methods. A total of 12,123 related articles published from 1976 to 2023 were retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases. Co-occurrence analysis and burst detection were conducted on the literature using the VOSviewer and CiteSpace tools to identify the research trends in geological hazard monitoring technologies. The results reveal that “data fusion”, “landslide identification”, “deep learning”, and “risk early warning” are currently the main research hot spots. Additionally, the combined application of Global Navigation Satellite System (GNSS) and Real-Time Kinematic (RTK) technologies, as well as GNSS and Long Short-Term Memory (LSTM) models, were identified as important directions for future research. The bibliometric perspective offers a systematic theoretical framework and technical guidance for future research, thereby facilitating the sustainable advancement of safety, security, and disaster management. Full article

The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops a region-specific framework by integrating SHAP (SHapley Additive exPlanation) analysis with twelve landslide conditioning factors (LCFs) and three progressive sampling strategies, aiming to create adaptive non-landslide point selection criteria tailored to unique environmental and geological characteristics. The strategies include (1) multi-ratio random sampling (1:1 to 1:200), (2) susceptibility-based sampling adjustments derived from pre-susceptibility analysis, and (3) LCF-based correction using the NDVI threshold identified through SHAP analysis. Results show that LCF-based correction achieved the highest performance, while a 1:5 ratio proved optimal in random sampling, aligning with regional characteristics. This framework demonstrates the importance of region-specific sampling strategies in improving landslide susceptibility prediction. Full article

The phenomenon of landslide spatial aggregation is widespread in nature, which can affect the result of landslide susceptibility prediction (LSP). In order to eliminate the uncertainty caused by landslide spatial aggregation in an LSP study, researchers have put forward some techniques to quantify the degree of landslide spatial aggregation, including the class landslide aggregation index (LAI), which is widely used. However, due to the limitations of the existing LAI method, it is still uncertain when applied to the LSP study of the area with complex engineering geological conditions. Considering landslide spatial aggregation, a new method, the dual-frequency ratio (DFR), was proposed to establish the association between the occurrence of landslides and twelve predisposing factors (i.e., slope, aspect, elevation, relief amplitude, engineering geological rock group, fault density, river density, average annual rainfall, NDVI, distance to road, quarry density and hydropower station density). And in the DFR method, an improved LAI was used to quantify the degree of landslide spatial aggregation in the form of a frequency ratio. Taking the middle reaches of the Tarim River Basin as the study area, the application of the DFR method in an LSP study was verified. Meanwhile, four models were adopted to calculate the landslide susceptibility indexes (LSIs) in this study, including frequency ratio (FR), the analytic hierarchy process (AHP), logistic regression (LR) and random forest (RF). Finally, the receiver operating characteristic curves (ROCs) and distribution patterns of LSIs were used to assess each LSP model’s prediction performance. The results showed that the DFR method could reduce the adverse effect of landslide spatial aggregation on the LSP study and better enhance the LSP model’s prediction performance. Additionally, models of LR and RF had a superior prediction performance, among which the DFR-RF model had the highest prediction accuracy value, and a quite reliable result of LSIs. Full article

When a landslide mass enters a water body, it generates waves that propagate along the river channel, climb up upon reaching the riverbank, and impact nearby residential areas. To investigate the characteristics of wave run-up on a three-dimensional terrain, this study established a large-scale 3D physical model with a scale of 1:150 (dimensions: 64 m × 40 m × 3 m) based on the geological features of a specific amphibious landslide. The results show that the landslide-induced waves can partially inundate nearby residential areas. The unique terrain formed by the combination of residential areas and the southern riverbank amplifies the wave run-up height. A predictive formula was used to estimate the wave run-up height during wave convergence. This study provides valuable insights for predicting wave run-up heights in three-dimensional terrains. Considering the influence of different water levels on wave run-up, the study can be used to optimize water level regulation. Full article

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. Full article

The effectiveness of data-driven landslide susceptibility mapping relies on data integrity and advanced geospatial analysis; however, selecting the most suitable method and identifying key regional factors remains a challenging task. To address this, this study assessed the performance of six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF and CNN+CatBoost), and a Stacking Ensemble (SE) combining CNN, RF, and CatBoost in mapping landslide susceptibility along the Karakoram Highway in northern Pakistan. Twelve geospatial factors were examined, categorized into Topography/Geomorphology, Land Cover/Vegetation, Geology, Hydrology, and Anthropogenic Influence. A detailed landslide inventory of 272 occurrences was compiled to train the models. The proposed stacking ensemble and hybrid models improve landslide susceptibility modeling, with the stacking ensemble achieving an AUC of 0.91. Hybrid modeling enhances accuracy, with CNN–RF boosting RF’s AUC from 0.85 to 0.89 and CNN–CatBoost increasing CatBoost’s AUC from 0.87 to 0.90. Chi-square (χ²) values (9.8–21.2) and p-values (<0.005) confirm statistical significance across models. This study identifies approximately 20.70% of the area as from high to very high risk, with the SE model excelling in detecting high-risk zones. Key factors influencing landslide susceptibility showed slight variations across the models, while multicollinearity among variables remained minimal. The proposed modeling approach reduces uncertainties, enhances prediction accuracy, and supports decision-makers in implementing effective landslide mitigation strategies. Full article

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets. Full article

Slope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to missing data that can compromise analysis accuracy and reliability. This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. Sensors installed at two field locations, Yangyang and Omi, provided high-resolution displacement data, with approximately 34,000 data points per sensor. We simulated missing data scenarios at rates of 1%, 3%, 5%, and 10%, reflecting both random and block missing patterns to mimic realistic conditions. The imputation performance of each model was evaluated using Mean Absolute Error, Mean Squared Error, and Root Mean Square Error to assess accuracy and robustness across varying levels of data loss. Results demonstrate that each model has distinct advantages under specific missingness patterns, with the ImputeFormer model showing strong performance in capturing long-term dependencies. These findings underscore the potential of machine learning-based imputation methods to maintain data integrity in slope displacement monitoring, supporting reliable slope stability assessments even in the presence of significant data gaps. This research offers insights into the optimal selection and application of imputation models for enhancing the quality and continuity of geotechnical monitoring data. 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

Due to Paleo-clay’s unique properties and widespread distribution throughout China, it is essential in geotechnical engineering. Rainfall frequently causes the deformation of Paleo-clay slopes, making slope instability prediction crucial for disaster prevention. This study explored Paleo-clay’s strength degradation and slope stability under soaking and wet–dry cycles. Using Mohr–Coulomb failure envelopes from experiments, curve fitting was used to find the patterns of Paleo-clay strength degradation. Finite element simulations and the strength discounting method were used to analyze the stability and deformation of Paleo-clay slopes. The results indicate that wet–dry cycles impact them more than soaking. Paleo-clay’s cohesion decreases exponentially as the number of wet–dry cycles and soaking times rise, but the internal friction angle changes very little. After 10 wet–dry cycles and 24 days of soaking, iron-bearing clay’s cohesion decreased to 17% and 44% and reticular clay’s to 32% and 48%. Based on the study area characteristics, three slope types were constructed. Their stability exhibited exponential decay. Under soaking, stability remained above 1.4; under wet–dry cycles, type I and II stability fell below 1.0, leading to deformation and failure. All types showed traction landslides with sliding zones transitioning from deep to shallow. Practical engineering should focus on the shallow failures of Paleo-clay slopes. Full article

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. We assessed the impact of SAC at three crucial phases of ML modeling: hyperparameter tuning, performance evaluation, and learning curve analysis. As an alternative to R-CV, we used spatial cross-validation (S-CV). This method considers SAC when partitioning the training and testing subsets. This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. R-CV also occasionally fails to reveal the true importance of the hyperparameters of models such as SVM and ANN. Additionally, R-CV falsely portrays a considerable improvement in model performance as the number of variables increases. However, this was not the case when the models were evaluated using S-CV. The impact of SAC was more noticeable in complex models such as SVM, RF, and C5.0 (except for ANN) than in simple models such as LDA and LR (except for KNN). Overall, we recommend S-CV over R-CV for a reliable assessment of ML model performance in large-scale LSM. Full article