Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong d...Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.展开更多
Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchm...Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchments,this study evaluates the relative contributions of landslide-derived and channel-derived sediment in controlling debris-flow fan magnitude,and quantifies sediment supply during the 2023 rainy season using DEM differencing.A total of 766 landslides occurred predominantly on slopes of 40°-50°and southeast-southwest aspects,generating 36.17×10^(4)m^(3)of material.Gully heads exhibit exceptionally lower landscape dissection thresholds compared with loess and Quaternary regions in China,indicating high susceptibility to failure under intensified runoff.The results show that Landslide area-volume scaling exponent(b)varies with hillslope geometry(K_(u)):b>1.3 for K_(u)<8 and generally b<1.3 for K_(u)>8,indicating more complete scar evacuation upslope and partial erosion downslope.Despite the abundance of landslides,their contribution to debris flow fan magnitude is minor(<25%),with channel debris dominating(>75%).DEM differencing of a small catchment before and after the 2023 rainy season further reveals that sediment supply originates primarily from the main channel(60.6%)and tributaries(23.3%),with smaller contributions from channel banks(6.8%)and channel heads(9.2%).Tributaries exhibit the greatest mean erosion depth(4.2 m),exceeding that of the main channel(3.8 m).These findings demonstrate that debris-flow material supply in the Daheba Basin is transport-limited and controlled mainly by fluvial entrainment rather than slope failures.Climatic warming and wetting may enhance slope instability,but sediment mobilization is dominantly governed by runoff-driven channel erosion.This study underscores the importance of prioritizing channel sediment dynamics in debris flow hazards assessments for arid regions of the Tibetan plateau.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitig...Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitigation.InSAR technology has been extensively employed to monitor surface deformations in reservoir landslides.However,the accuracy of InSAR-derived deformation fields is often limited by the reliability of prior deformation model.Traditional models,which primarily rely on linear or periodic function,frequently overlook the step-like evolution characteristics of reservoir landslides.To address this limitation,this study introduces a multi-temporal InSAR approach that incorporates Sigmoid function to enhance the deformation modeling of reservoir landslides.To solve the nonlinear parameters within the model,Taylor series expansion-based observation equation is constructed to estimate these parameters accurately.The proposed model was evaluated using both the simulated and real datasets from the Hongyanzi landslide in the Pubugou reservoir area.The results demonstrate that the proposed model significantly improves the accuracies of parameter estimation and deformation time-series.Experiments conducted under the sensitivity of interferogram stacks and varying atmospheric phase screen interference magnitudes further confirm the proposed model’s robustness and application potential.In addition,the sensitivity analysis of the initial parameters in the real data experiment scenario demonstrates the robustness of the proposed model’s nonlinear parameter estimation.Finally,the cross-correlation analysis reveals that the deformation of the Hongyanzi landslide is triggered by the decline of the reservoir water level,and quantitatively evaluates the lag time between the deformation and the reservoir water level.Our results offer novel insights for InSAR monitoring of other complex deformation evolution scenarios.Prior information is incorporated into the deformation modeling to estimate a more reliable InSAR deformation field.展开更多
Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the ten...Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the tensile stress in the area has exceeded the tensile strength of the soil,leading to local instability.To explore the impact of tensile failure behavior on the stability and failure modes of reservoir landslides,the Huangtupo Riverside Slump#1 is taken as a case study.By considering local tensile failure,potential tensile cracks are incorporated into the analysis via the limit equilibrium method and reliability theory.The reliability of landslides under different tensile failure scenarios is quantified.Strain-softening characteristics of the soil are combined to further analyze the failure transmission path of the landslide.Finally,these potential failure modes were validated through physical model tests.The results show that cracks developing at rear positions reduce the stability of the slope and increase the probability of instability.During the destruction process,retrogressive failures with multiple sliding surfaces are likely to occur.However,tensile failure at the forefront reduces the likelihood of an individual slide mass descending.Progressive failure results in both regular and skip transmission patterns.Additionally,cracks and water level changes can also lead to shifts in the positions of the most dangerous blocks.Therefore,in practical landslide analysis and prevention,it is necessary to consider local tensile damage and identify potential tensile crack locations in advance to optimize prevention measures and accurately evaluate landslide risk.展开更多
Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing M...Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing MRNs vulnerability under RILH disturbances are still lacking.To bridge this gap,this study develops a Cascading Failure Model for Rainfall-Induced Landslide Hazard(CFM-RILH).Validation via a case study of the GarzêTibetan Autonomous Prefecture Road Network(GTPRNs)reveals key characteristics of MRNs system vulnerability under RILH disturbances:(1)Under the disturbance effects of RILH,the vulnerability of the MRNs system follows a nonlinear phase transition law that intensifies with increasing disturbance intensity,exhibiting a distinct critical threshold.When the disturbance intensity exceeds this threshold,the system undergoes a global cascading failure phenomenon analogous to an“avalanche.”(2)Under RILH disturbances,the robustness of the MRNs system possesses a distinct safety boundary.Exceeding this boundary not only fails to improve hazard resistance but instead substantially elevates the risk of large-scale cascading failure.(3)Increasing network redundancy may be considered one of the primary engineering measures for enhancing MRNs resilience against such disturbances.Based on these findings,we propose a“Two-Stage Emergency Response and Hierarchical Fortification”strategy specifically to improve the resilience of GTPRNs impacted by RILH.The CFM-RILH model provides an effective tool for assessing road network vulnerability under such hazards.Furthermore,its modeling framework can also inform vulnerability assessment and resilience strategy development for road networks affected by other types of slope hazards.展开更多
Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes,where segmentati...Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes,where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions.To address these issues,we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder,guided multimodal fusion,and deep supervision.The framework is built upon the synergistic combination of cross-attention,gated fusion,and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation,enabling efficient context modeling and robust feature exchange between modalities.The decoder captures long-range context at deeper levels using lightweight cross-attention and refines spatial details at shallower levels through attention-gated skip fusion,enabling precise boundary delineation and fewer false positives.The gated fusion further enhances multimodal integration of optical and topographical cues,and the deep supervision stabilizes training and improves generalization.Moreover,to mitigate checkerboard artifacts,a learnable sub-pixel upsampling is devised to replace the traditional transposed convolution.Despite its compact design with fewer parameters,the model consistently outperforms state-of-the-art baselines.Experiments on two benchmark datasets,Landslide4Sense and Bijie,confirm the effectiveness of the framework.On the Bijie dataset,it achieves an F1-score of 0.9110 and an intersection over union(IoU)of 0.8839.These results highlight its potential for accurate large-scale landslide inventory mapping and real-time disaster response.The implementation is publicly available at https://github.com/mishaown/DiGATe-UNet-LandSlide-Segmentation(accessed on 3 November 2025).展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
Low–angle submarine landslides pose a greater threat to offshore infrastructure compared to those with steep sliding angles.Understanding the preparation and triggering mechanism of these low–angle submarine landsli...Low–angle submarine landslides pose a greater threat to offshore infrastructure compared to those with steep sliding angles.Understanding the preparation and triggering mechanism of these low–angle submarine landslides remains a significant challenge.This study focuses on a deformed low–angle submarine landslide in the shelf–slope break of the Pearl River Mouth Basin,South China Sea,integrating sedimentology,geophysics,and geotechnology to investigate potential failure mechanisms.The architecture and deformation characteristics of the submarine landslide were elucidated by analyzing multibeam and seismic data.Within the context of the regional geological history and tectonic framework,this study focuses on the factors(e.g.,rapid sedimentation,fluid activity,and earthquakes)that potentially contributed to the submarine slope failure.Furthermore,a series of stability evaluations considering the effects of rapid sedimentation and earthquakes was conducted.Our findings indicate that the most probable triggering mechanism involves the combined effects of sedimentation controlled by sea–level fluctuations,high–pressure gas activity,and seismic events.The high–pressure gas,which acts as a long–term preconditioning factor by elevating pore pressures and reducing shear resistance within the sediment,accumulated beneath the upper and middle sections of the low–permeability stratum that was formed during sea–level rise and ultimately evolved into the sliding mass.The overpressure generated by gas accumulation predisposed the submarine slope to instability,and a frequent or moderate earthquake ultimately initiated local failure.This study enhances the mechanistic understanding of low–angle slope failures in the shelf–slope break zone and provides critical insights for assessing marine hazard risks.展开更多
On September 5,2022,at least 10,855 landslides had been triggered by a magnitude Mw 6.7(Ms 6.8)earthquake on the eastern margin of the Tibetan Plateau.Unfortunately,a detailed analysis of the spatial patterns of lands...On September 5,2022,at least 10,855 landslides had been triggered by a magnitude Mw 6.7(Ms 6.8)earthquake on the eastern margin of the Tibetan Plateau.Unfortunately,a detailed analysis of the spatial patterns of landslides in the eastern margin of the Baryan Har block is lacking.The observations show that the highest landslide concentrations are distributed along the seismogenic fault(Moxi fault)and Dadu River valley,coinciding with the effects of the hanging wall and microepicenter.Seismogenic tectonics controlled the regional distribution of new landslides,and the local topography influenced the detailed positions on the slopes.The total landslide mass wasting volume was 223.1×10^(6)m^(3),and the maximum occurred in the Wandong Basin(value of 74×10^(6)m^(3)).Thirty landslide dams were temporarily existing.Although some local collapses occurred at the toe of the Hailuogou glacier,seismic shaking had no obvious influence on the overall stability of the glacier.A post debris flow assessment indicates that some large basins contained much loose material and that some steep small basins had high debris flow susceptibility.On the eastern margin of the Bayan Har block,the landslide-triggering thrust and strike-slip events both follow the distributions of the hanging wall.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
The strength of the sliding zone soil determines the stability of reservoir landslides.Fluctuations in water levels cause a change in the seepage field,which serves as both the external hydrogeological environment and...The strength of the sliding zone soil determines the stability of reservoir landslides.Fluctuations in water levels cause a change in the seepage field,which serves as both the external hydrogeological environment and the internal component of a landslide.Therefore,considering the strength changes of the sliding zone with seepage effects,they correspond with the actual hydrogeological circumstances.To investigate the shear behavior of sliding zone soil under various seepage pressures,24 samples were conducted by a self-developed apparatus to observe the shear strength and measure the permeability coefficients at different deformation stages.After seepage-shear tests,the composition of clay minerals and microscopic structure on the shear surface were analyzed through X-ray and scanning electron microscope(SEM)to understand the coupling effects of seepage on strength.The results revealed that the sliding zone soil exhibited strain-hardening without seepage pressure.However,the introduction of seepage caused a significant reduction in shear strength,resulting in strain-softening characterized by a three-stage process.Long-term seepage action softened clay particles and transported broken particles into effective seepage channels,causing continuous damage to the interior structure and reducing the permeability coefficient.Increased seepage pressure decreased the peak strength by disrupting occlusal and frictional forces between sliding zone soil particles,which carried away more clay particles,contributing to an overhead structure in the soil that raised the permeability coefficient and decreased residual strength.The internal friction angle was less sensitive to variations in seepage pressure than cohesion.展开更多
Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the...Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.展开更多
Retrogressive landslides in sensitive clays pose significant risks to nearby infrastructure,as natural toe erosion or localized disturbances can trigger progressive block failures.While prior studies have largely reli...Retrogressive landslides in sensitive clays pose significant risks to nearby infrastructure,as natural toe erosion or localized disturbances can trigger progressive block failures.While prior studies have largely relied on two-dimensional(2D)large-deformation analyses,such models overlook key three-dimensional(3D)failure mechanisms and variability effects.This study develops a 3D probabilistic framework by integrating the Coupled Eulerian–Lagrangian(CEL)method with random field theory to simulate retrogressive landslides in spatially variable clay.Using Monte Carlo simulations,we compare 2D and 3D random large-deformation models to evaluate failure modes,runout distances,sliding velocities,and influence zones.The 3D analyses captured more complex failure modes—such as lateral retrogression and asynchronous block mobilization across slope width.Additionally,the 3D analyses predict longer mean runout distances(13.76 vs.11.92 m),wider mean influence distance(11.35 vs.8.73 m),and higher mean sliding velocities(4.66 vs.3.94 m/s)than their 2D counterparts.Moreover,3D models exhibit lower coefficients of variation(e.g.,0.10 for runout distance)due to spatial averaging across slope width.Probabilistic hazard assessment shows that 2D models significantly underpredict near-field failure probabilities(e.g.,48.8%vs.89.9%at 12 m from the slope toe).These findings highlight the limitations of 2D analyses and the importance of multi-directional spatial variability for robust geohazard assessments.The proposed 3D framework enables more realistic prediction of landslide mobility and supports the design of safer,risk-informed infrastructure.展开更多
Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferom...Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.展开更多
The original online version https://doi.org/10.1007/s11629-024-9130-x has wrong title.The correct title for this article should be“Reactivation mechanisms of the ancient Dahekou landslide in Hanzhong City,Shaanxi Pro...The original online version https://doi.org/10.1007/s11629-024-9130-x has wrong title.The correct title for this article should be“Reactivation mechanisms of the ancient Dahekou landslide in Hanzhong City,Shaanxi Province,China”.展开更多
文摘Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.
基金supported by the Chengdu University of Information Technology Doctoral Fund‘Study on the Initiation Mechanism of Hydraulic Debris Flow Based on Shields Stress’(KYTZ202275)the Second Tibetan Scientific Expedition and Research Program(Grant No.2019QZKK0902)。
文摘Debris flows have increased in frequency within the arid Daheba Basin on the northeastern Tibetan Plateau,but their sediment sources remain poorly quantified.Using high-resolution UAV-derived DEMs from 51 small catchments,this study evaluates the relative contributions of landslide-derived and channel-derived sediment in controlling debris-flow fan magnitude,and quantifies sediment supply during the 2023 rainy season using DEM differencing.A total of 766 landslides occurred predominantly on slopes of 40°-50°and southeast-southwest aspects,generating 36.17×10^(4)m^(3)of material.Gully heads exhibit exceptionally lower landscape dissection thresholds compared with loess and Quaternary regions in China,indicating high susceptibility to failure under intensified runoff.The results show that Landslide area-volume scaling exponent(b)varies with hillslope geometry(K_(u)):b>1.3 for K_(u)<8 and generally b<1.3 for K_(u)>8,indicating more complete scar evacuation upslope and partial erosion downslope.Despite the abundance of landslides,their contribution to debris flow fan magnitude is minor(<25%),with channel debris dominating(>75%).DEM differencing of a small catchment before and after the 2023 rainy season further reveals that sediment supply originates primarily from the main channel(60.6%)and tributaries(23.3%),with smaller contributions from channel banks(6.8%)and channel heads(9.2%).Tributaries exhibit the greatest mean erosion depth(4.2 m),exceeding that of the main channel(3.8 m).These findings demonstrate that debris-flow material supply in the Daheba Basin is transport-limited and controlled mainly by fluvial entrainment rather than slope failures.Climatic warming and wetting may enhance slope instability,but sediment mobilization is dominantly governed by runoff-driven channel erosion.This study underscores the importance of prioritizing channel sediment dynamics in debris flow hazards assessments for arid regions of the Tibetan plateau.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
基金supported by the National Natural Science Foundation of China(Grant Nos.42474054,42030112)the National Key Research and Development Program Project(Grant No.2021YFC3000500)+3 种基金the Science and Technology Innovation Program of Hunan Province(Grant No.2023SK2012)the Nature Science Foundation of Hunan Province(Grant No.2024JJ6411)the Research Foundation of Education Bureau of Hunan Province(Grant No.23C0295)the Nature Science Foundation of Shaoyang City(Grant No.2024PT6099).
文摘Reservoir landslides are significant geological hazards that pose severe risks to reservoir safety.Detecting the spatial-temporal evolution of slope movement is crucial for effective risk assessment and disaster mitigation.InSAR technology has been extensively employed to monitor surface deformations in reservoir landslides.However,the accuracy of InSAR-derived deformation fields is often limited by the reliability of prior deformation model.Traditional models,which primarily rely on linear or periodic function,frequently overlook the step-like evolution characteristics of reservoir landslides.To address this limitation,this study introduces a multi-temporal InSAR approach that incorporates Sigmoid function to enhance the deformation modeling of reservoir landslides.To solve the nonlinear parameters within the model,Taylor series expansion-based observation equation is constructed to estimate these parameters accurately.The proposed model was evaluated using both the simulated and real datasets from the Hongyanzi landslide in the Pubugou reservoir area.The results demonstrate that the proposed model significantly improves the accuracies of parameter estimation and deformation time-series.Experiments conducted under the sensitivity of interferogram stacks and varying atmospheric phase screen interference magnitudes further confirm the proposed model’s robustness and application potential.In addition,the sensitivity analysis of the initial parameters in the real data experiment scenario demonstrates the robustness of the proposed model’s nonlinear parameter estimation.Finally,the cross-correlation analysis reveals that the deformation of the Hongyanzi landslide is triggered by the decline of the reservoir water level,and quantitatively evaluates the lag time between the deformation and the reservoir water level.Our results offer novel insights for InSAR monitoring of other complex deformation evolution scenarios.Prior information is incorporated into the deformation modeling to estimate a more reliable InSAR deformation field.
基金supported by the Major Program of National Natural Science Foundation of China(Grant No.42090055)the National Key ScientificInstruments and Equipment Development Projects of China(Grant No.41827808)the National Nature Science Foundation of China(Grant No.42207216).
文摘Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the tensile stress in the area has exceeded the tensile strength of the soil,leading to local instability.To explore the impact of tensile failure behavior on the stability and failure modes of reservoir landslides,the Huangtupo Riverside Slump#1 is taken as a case study.By considering local tensile failure,potential tensile cracks are incorporated into the analysis via the limit equilibrium method and reliability theory.The reliability of landslides under different tensile failure scenarios is quantified.Strain-softening characteristics of the soil are combined to further analyze the failure transmission path of the landslide.Finally,these potential failure modes were validated through physical model tests.The results show that cracks developing at rear positions reduce the stability of the slope and increase the probability of instability.During the destruction process,retrogressive failures with multiple sliding surfaces are likely to occur.However,tensile failure at the forefront reduces the likelihood of an individual slide mass descending.Progressive failure results in both regular and skip transmission patterns.Additionally,cracks and water level changes can also lead to shifts in the positions of the most dangerous blocks.Therefore,in practical landslide analysis and prevention,it is necessary to consider local tensile damage and identify potential tensile crack locations in advance to optimize prevention measures and accurately evaluate landslide risk.
基金financially supported by the National Key R&D Program of China(2024YFE0111900)The National Natural Science Foundation of China(U2468214,52378370,52278372)+1 种基金The National Ten Thousand Talent Program for Young Top-notch Talents(2022QB04978)The Science and Technology Program of Hebei Province(2023HBQZYCSB004)。
文摘Global climate change is intensifying the impact of slope hazards,particularly rainfall-induced landslide hazards(RILH),on mountain road networks(MRNs).However,effective quantitative models for dynamically assessing MRNs vulnerability under RILH disturbances are still lacking.To bridge this gap,this study develops a Cascading Failure Model for Rainfall-Induced Landslide Hazard(CFM-RILH).Validation via a case study of the GarzêTibetan Autonomous Prefecture Road Network(GTPRNs)reveals key characteristics of MRNs system vulnerability under RILH disturbances:(1)Under the disturbance effects of RILH,the vulnerability of the MRNs system follows a nonlinear phase transition law that intensifies with increasing disturbance intensity,exhibiting a distinct critical threshold.When the disturbance intensity exceeds this threshold,the system undergoes a global cascading failure phenomenon analogous to an“avalanche.”(2)Under RILH disturbances,the robustness of the MRNs system possesses a distinct safety boundary.Exceeding this boundary not only fails to improve hazard resistance but instead substantially elevates the risk of large-scale cascading failure.(3)Increasing network redundancy may be considered one of the primary engineering measures for enhancing MRNs resilience against such disturbances.Based on these findings,we propose a“Two-Stage Emergency Response and Hierarchical Fortification”strategy specifically to improve the resilience of GTPRNs impacted by RILH.The CFM-RILH model provides an effective tool for assessing road network vulnerability under such hazards.Furthermore,its modeling framework can also inform vulnerability assessment and resilience strategy development for road networks affected by other types of slope hazards.
基金funded by the National Natural Science Foundation of China,grant number 62262045the Fundamental Research Funds for the Central Universities,grant number 2023CDJYGRH-YB11the Open Funding of SUGON Industrial Control and Security Center,grant number CUIT-SICSC-2025-03.
文摘Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes,where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions.To address these issues,we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder,guided multimodal fusion,and deep supervision.The framework is built upon the synergistic combination of cross-attention,gated fusion,and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation,enabling efficient context modeling and robust feature exchange between modalities.The decoder captures long-range context at deeper levels using lightweight cross-attention and refines spatial details at shallower levels through attention-gated skip fusion,enabling precise boundary delineation and fewer false positives.The gated fusion further enhances multimodal integration of optical and topographical cues,and the deep supervision stabilizes training and improves generalization.Moreover,to mitigate checkerboard artifacts,a learnable sub-pixel upsampling is devised to replace the traditional transposed convolution.Despite its compact design with fewer parameters,the model consistently outperforms state-of-the-art baselines.Experiments on two benchmark datasets,Landslide4Sense and Bijie,confirm the effectiveness of the framework.On the Bijie dataset,it achieves an F1-score of 0.9110 and an intersection over union(IoU)of 0.8839.These results highlight its potential for accurate large-scale landslide inventory mapping and real-time disaster response.The implementation is publicly available at https://github.com/mishaown/DiGATe-UNet-LandSlide-Segmentation(accessed on 3 November 2025).
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
基金supported by NSFC Shiptime Sharing Project(Nos.42349302,and 42149905)the Hubei Provincial Natural Science Foundation of China(No.2024AFB515)+4 种基金the National Natural Science Foundation of China(Nos.42207173,41831280,and 42176071)the National Key R&D Program of China(No.2024YFC3082500)the Shandong Provincial Natural Science Foundation of China(No.ZR2022QD002)the Shandong Provincial Taishan Scholar Construction Project(No.tsqn202507091)the Shandong Provincial Young Innovators Team(No.2024KJH183).
文摘Low–angle submarine landslides pose a greater threat to offshore infrastructure compared to those with steep sliding angles.Understanding the preparation and triggering mechanism of these low–angle submarine landslides remains a significant challenge.This study focuses on a deformed low–angle submarine landslide in the shelf–slope break of the Pearl River Mouth Basin,South China Sea,integrating sedimentology,geophysics,and geotechnology to investigate potential failure mechanisms.The architecture and deformation characteristics of the submarine landslide were elucidated by analyzing multibeam and seismic data.Within the context of the regional geological history and tectonic framework,this study focuses on the factors(e.g.,rapid sedimentation,fluid activity,and earthquakes)that potentially contributed to the submarine slope failure.Furthermore,a series of stability evaluations considering the effects of rapid sedimentation and earthquakes was conducted.Our findings indicate that the most probable triggering mechanism involves the combined effects of sedimentation controlled by sea–level fluctuations,high–pressure gas activity,and seismic events.The high–pressure gas,which acts as a long–term preconditioning factor by elevating pore pressures and reducing shear resistance within the sediment,accumulated beneath the upper and middle sections of the low–permeability stratum that was formed during sea–level rise and ultimately evolved into the sliding mass.The overpressure generated by gas accumulation predisposed the submarine slope to instability,and a frequent or moderate earthquake ultimately initiated local failure.This study enhances the mechanistic understanding of low–angle slope failures in the shelf–slope break zone and provides critical insights for assessing marine hazard risks.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20603 and U21A2008)the Science Technology Research Program of the Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Grant No.IMHE-ZYTS-03).
文摘On September 5,2022,at least 10,855 landslides had been triggered by a magnitude Mw 6.7(Ms 6.8)earthquake on the eastern margin of the Tibetan Plateau.Unfortunately,a detailed analysis of the spatial patterns of landslides in the eastern margin of the Baryan Har block is lacking.The observations show that the highest landslide concentrations are distributed along the seismogenic fault(Moxi fault)and Dadu River valley,coinciding with the effects of the hanging wall and microepicenter.Seismogenic tectonics controlled the regional distribution of new landslides,and the local topography influenced the detailed positions on the slopes.The total landslide mass wasting volume was 223.1×10^(6)m^(3),and the maximum occurred in the Wandong Basin(value of 74×10^(6)m^(3)).Thirty landslide dams were temporarily existing.Although some local collapses occurred at the toe of the Hailuogou glacier,seismic shaking had no obvious influence on the overall stability of the glacier.A post debris flow assessment indicates that some large basins contained much loose material and that some steep small basins had high debris flow susceptibility.On the eastern margin of the Bayan Har block,the landslide-triggering thrust and strike-slip events both follow the distributions of the hanging wall.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金supported by the Major Program of the National Natural Science Foundation of China (Grant No.42090055)the National Major Scientific Instruments and Equipment Development Projects of China (Grant No.41827808)the National Nature Science Foundation of China (Grant No.42207216).
文摘The strength of the sliding zone soil determines the stability of reservoir landslides.Fluctuations in water levels cause a change in the seepage field,which serves as both the external hydrogeological environment and the internal component of a landslide.Therefore,considering the strength changes of the sliding zone with seepage effects,they correspond with the actual hydrogeological circumstances.To investigate the shear behavior of sliding zone soil under various seepage pressures,24 samples were conducted by a self-developed apparatus to observe the shear strength and measure the permeability coefficients at different deformation stages.After seepage-shear tests,the composition of clay minerals and microscopic structure on the shear surface were analyzed through X-ray and scanning electron microscope(SEM)to understand the coupling effects of seepage on strength.The results revealed that the sliding zone soil exhibited strain-hardening without seepage pressure.However,the introduction of seepage caused a significant reduction in shear strength,resulting in strain-softening characterized by a three-stage process.Long-term seepage action softened clay particles and transported broken particles into effective seepage channels,causing continuous damage to the interior structure and reducing the permeability coefficient.Increased seepage pressure decreased the peak strength by disrupting occlusal and frictional forces between sliding zone soil particles,which carried away more clay particles,contributing to an overhead structure in the soil that raised the permeability coefficient and decreased residual strength.The internal friction angle was less sensitive to variations in seepage pressure than cohesion.
基金the financial support from the Fujian Science Foundation for Outstanding Youth(2023J06039)the National Natural Science Foundation of China(Grant No.41977259,U2005205,41972268)the Independent Research Project of Technology Innovation Center for Monitoring and Restoration Engineering of Ecological Fragile Zone in Southeast China(KY-090000-04-2022-019)。
文摘Shotcrete is one of the common solutions for shallow sliding.It works by forming a protective layer with high strength and cementing the loose soil particles on the slope surface to prevent shallow sliding.However,the solidification time of conventional cement paste is long when shotcrete is used to treat cohesionless soil landslide.The idea of reinforcing slope with polyurethane solidified soil(i.e.,mixture of polyurethane and sand)was proposed.Model tests and finite element analysis were carried out to study the effectiveness of the proposed new method on the emergency treatment of cohesionless soil landslide.Surcharge loading on the crest of the slope was applied step by step until landslide was triggered so as to test and compare the stability and bearing capacity of slope models with different conditions.The simulated slope displacements were relatively close to the measured results,and the simulated slope deformation characteristics were in good agreement with the observed phenomena,which verifies the accuracy of the numerical method.Under the condition of surcharge loading on the crest of the slope,the unreinforced slope slid when the surcharge loading exceeded 30 k Pa,which presented a failure mode of local instability and collapse at the shallow layer of slope top.The reinforced slope remained stable even when the surcharge loading reached 48 k Pa.The displacement of the reinforced slope was reduced by more than 95%.Overall,this study verifies the effectiveness of polyurethane in the emergency treatment of cohesionless soil landslide and should have broad application prospects in the field of geological disasters concerning the safety of people's live.
基金supported by the National Key Research and Development Program of China(No.2024YFC2815400)the European Commission(Nos.HORIZON MSCA-2024-PF-01 and 101200637)+2 种基金the Opening Fund of the State Key Laboratory of Water Resources Engineering and Management at Wuhan University(No.2024SGG07)the Shandong Provincial Natural Science Foundation(No.ZR2025MS647)the Sand Hazards and Opportunities for Resilience,Energy,and Sustainability(SHORES)Center,funded by Tamkeen under the NYUAD Research Institute Award CG013.
文摘Retrogressive landslides in sensitive clays pose significant risks to nearby infrastructure,as natural toe erosion or localized disturbances can trigger progressive block failures.While prior studies have largely relied on two-dimensional(2D)large-deformation analyses,such models overlook key three-dimensional(3D)failure mechanisms and variability effects.This study develops a 3D probabilistic framework by integrating the Coupled Eulerian–Lagrangian(CEL)method with random field theory to simulate retrogressive landslides in spatially variable clay.Using Monte Carlo simulations,we compare 2D and 3D random large-deformation models to evaluate failure modes,runout distances,sliding velocities,and influence zones.The 3D analyses captured more complex failure modes—such as lateral retrogression and asynchronous block mobilization across slope width.Additionally,the 3D analyses predict longer mean runout distances(13.76 vs.11.92 m),wider mean influence distance(11.35 vs.8.73 m),and higher mean sliding velocities(4.66 vs.3.94 m/s)than their 2D counterparts.Moreover,3D models exhibit lower coefficients of variation(e.g.,0.10 for runout distance)due to spatial averaging across slope width.Probabilistic hazard assessment shows that 2D models significantly underpredict near-field failure probabilities(e.g.,48.8%vs.89.9%at 12 m from the slope toe).These findings highlight the limitations of 2D analyses and the importance of multi-directional spatial variability for robust geohazard assessments.The proposed 3D framework enables more realistic prediction of landslide mobility and supports the design of safer,risk-informed infrastructure.
基金jointly supported by the International Research Center of Big Data for Sustainable Development Goals(Grant No.CBAS2022GSP02)the National Natural Science Foundation of China(Grant Nos.42072320 and 42372264).
文摘Active landslides pose a significant threat globally,endangering lives and property.Effective monitoring and forecasting of displacements are essential for the timely warnings and mitigation of these events.Interferometric synthetic aperture radar(InSAR)stands out as an efficient and prevalent tool for monitoring landslide deformation and offers new prospects for displacement prediction.However,challenges such as inherent limitation of satellite viewing geometry,long revisit cycles,and limited data volume hinder its application in displacement forecasting,notably for landslides with near-north-south deformation less detectable by InSAR.To address these issues,we propose a novel strategy for predicting three-dimensional(3D)landslide displacement,integrating InSAR and global navigation satellite system(GNSS)measurements with machine learning(ML).This framework first synergizes InSAR line-of-sight(LOS)results with GNSS horizontal data to reconstruct 3D displacement time series.It then employs ML models to capture complex nonlinear relationships between external triggers,landslide evolutionary states,and 3D displacements,thus enabling accurate future deformation predictions.Utilizing four advanced ML algorithms,i.e.random forest(RF),support vector machine(SVM),long short-term memory(LSTM),and gated recurrent unit(GRU),with Bayesian optimization(BO)for hyperparameter tuning,we applied this innovative approach to the north-facing,slow-moving Xinpu landslide in the Three Gorges Reservoir Area(TGRA)of China.Leveraging over 6.5 years of Sentinel-1 satellite data and GNSS measurements,our framework demonstrates satisfactory and robust prediction performance,with an average root mean square deviation(RMSD)of 9.62 mm and a correlation coefficient(CC)of 0.996.This study presents a promising strategy for 3D displacement prediction,illustrating the efficacy of integrating InSAR monitoring with ML forecasting in enhancing landslide early warning capabilities.
文摘The original online version https://doi.org/10.1007/s11629-024-9130-x has wrong title.The correct title for this article should be“Reactivation mechanisms of the ancient Dahekou landslide in Hanzhong City,Shaanxi Province,China”.
