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
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
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
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
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
Landslides accompanying earthquakes are essential in landscape evolution along active fault zones.However,most studies focus on the rapid,catastrophic coseismic landslides with surface scars;the role of slow-moving la...Landslides accompanying earthquakes are essential in landscape evolution along active fault zones.However,most studies focus on the rapid,catastrophic coseismic landslides with surface scars;the role of slow-moving landslides and their relation with the coseismic landslides is poorly known.Combining radar interferometry,deep-learning network,and inventories of coseismic landslides,we show a clear complementary pattern between coseismic and slow-moving landslides distributed along the transition between the Qinghai-Xizang plateau and the Sichuan basin.Geomorphic analysis on areas dominated by coseismic and slow-moving landslides shows their distinct topographic fingerprints,suggesting that the coseismic landslides tend to occur on the top of the hill,while the slow-moving landslides erode the lower part of the slope.We infer that the coseismic landslides likely constrict the initiation and development of slow-moving landslides after large earthquakes by removing materials from slopes.Our results imply that the coseismic landslides may dominate the landscape feature close to active fault zones,where the lack of slow-moving landslides may indicate the historical occurrence of large-magnitude,landslide-prone earthquakes.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
Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation f...Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.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
Slow-moving landslides are widespread in the Mediterranean area,causing damage to the exposed facilities and economic losses in many countries.The recognition of slow-moving landslides in urban areas is always a diffi...Slow-moving landslides are widespread in the Mediterranean area,causing damage to the exposed facilities and economic losses in many countries.The recognition of slow-moving landslides in urban areas is always a difficult task to deal with because the presence of buildings,infrastructures,and human activities usually conceals the morphological signs of these landslide activities.So,in the last decades,numerous researchers have shown new methodologies to deepen the studies of similar instability phenomena.The present research is based on an integrated approach to investigate the landslide boundaries,type of movement,failure surface depth,and vulnerability state of buildings in Rota Greca Village(Calabria region,southern Italy) affected by a slowmoving landslide.For this purpose,multi-source data were acquired through geological and geomorphological surveys,recognition of landslide-induced damage on the built environment,subsurface investigations(e.g.,continuous drill boreholes,Standard Penetration Test,Rock Quality Designation index and inclinometer monitoring),laboratory tests(direct shear tests on undisturbed samples),geophysical survey,and InSAR-derived map of deformation rates.The complete integration of multi-source data allowed for the construction of reliable landslide modelling with relative geotechnical properties.In addition,the cross-comparison between surface deformation data by SAR images and severity damage level collected on the exposed buildings enabled to obtain the vulnerability map of the built area.In particular,the achieved goals highlighted two failure surfaces at about-13 and-25 m depth,causing a high vulnerability value for the buildings allocated in the central portion of the Rota Greca Village.The knowledge acquired by the multi-approach can be used to manage and implement appropriate landslide risk mitigation strategies,providing helpful advice and best practices to state-run organisations and stakeholders for landslide management in urban sites.展开更多
To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-dec...To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.展开更多
In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a mo...In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a model material suitable for analysing the deformation and failure of reservoir landslides,based on the existing research foundation of analogous materials,5 materials and 5 physical-mechanical parameters were selected to design an orthogonal test.The factor sensitivity of each component ratio and its influence on the physical-mechanical indices were studied by range analysis and stepwise regression analysis,and the proportioning method was determined.Finally,the model material was developed,and a model test was carried out considering Huangtupo as the prototype application.The results showed that(1)the model material composed of sand,barite powder,glass beads,clay,and bentonite had a wide distribution of physical-mechanical parameters,which could be applied to model tests under different conditions;(2)the physical-mechanical parameters of analogous materials matched the application prototype;and(3)the mechanical properties and seepage performance of the model material sample met the requirements of reservoir landslide model tests,which could be used to simulate landslide evolution and analyse the deformation process.展开更多
0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit freque...0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit frequency(Zhou et al.,2024;Dai et al.,2021).However,in densely vegetated or humid mountainous regions such as the Three Gorges Reservoir(TGR),C-band signals suffer from temporal decorrelation,limiting their effectiveness for landslide monitoring.展开更多
Landslides triggered by heavy rainfall pose a serious threat globally, endangering infrastructure and lives. Many previous landslide studies lack comprehensiveness and site specificity. Thus, a comprehensive investiga...Landslides triggered by heavy rainfall pose a serious threat globally, endangering infrastructure and lives. Many previous landslide studies lack comprehensiveness and site specificity. Thus, a comprehensive investigation is essential to understand the failure mechanisms and contributing factors for assessing potential future hazards. This study aims to investigate the debris flow landslide that occurred in Kavalappara, Kerala, India, on August 8, 2019, through an integrated approach combining geophysical test, weathering characterization, geotechnical, and numerical analyses. Shear wave velocity(V_s) was determined using the Multi-Channel Analysis of Surface Waves(MASW) test to obtain the substrata of the slope. Residual and unsaturated soil properties were obtained through ring shear and dew point potentiometer tests. The mineralogical composition of the soil was identified using Field-Emission Scanning Electron Microscopy(FE-SEM), Energy Dispersive XRay Analysis(EDAX), and X-Ray Diffraction(XRD) patterns. These investigation results focused on slope stability during rainfall infiltration using Limit Equilibrium(LEM) and Finite Element Analysis(FEM) for both low and high-intensity rainfall. Finally, the progressive failure mechanism of the landslide was analysed using the Finite Difference program(FDM). The soil profile showed a variation from loose to dense, with a V_(s) range of 172.85 m/s to 440.53 m/s. No rock layers were identified down to a depth of 15 m. The landslide area consists of migmatite as a parent rock, and the soil was identified as silty clay, comprising quartz and clay minerals. The FEM and LEM analyses reveal that the factor of safety was reduced to 0.83 due to increased pore water pressure and the degree of saturation. The pore water pressure ratio(r_(u)), estimated at 0.32, was used in the FDM. The landslide, initiated at r_u of 0.35, reached maximum velocities of 15.4 m/s horizontally and 12.4 m/s vertically. This study helps disaster management to analyse debris flow and find effective mitigation strategies for hilly areas.展开更多
The mechanism involved in deep-seated landslide-debris flow disaster chains has been studied for many years,however,it is still not completely understood.This study aims to analyze the key factors that were involved a...The mechanism involved in deep-seated landslide-debris flow disaster chains has been studied for many years,however,it is still not completely understood.This study aims to analyze the key factors that were involved and led to the geological disaster of Shaziba 62.0 m deep landslide-debris flow.Two extensive field investigations were conducted before and after the slope failure event.The study further used drilled cores,high-density resistivity method,and aerial photographs to obtain valuable insights into the disaster chain.It was found that opencast coal mining operations broke the locked segment of the front edge and heavy rainfall softened the slip zones along the faults.Mechanical calculations demonstrated that the coupling condition of the opencast coal mining and heavy rainfall triggered the landslide.A new evolution model was put forth to describe the complex mechanism of combining progressive retreat and tractive failure of hydraulic drive landslide,which was governed by the bedding-plane rock layer.Surface runoff caused the mass of the landslide to liquefy throughout the sliding process,resulting in overlapping deposits,debris-flow-barrier-lake,and erosion.These new insights led to the indication of a different triggering mechanism of landslides-debris flows,as well as laid the foundation for the proposed physical and mechanical mechanism model based on progressive retreat soil-rock mixed landslides with an upper locked segment and lower weak interlayer under heavy rainfall.展开更多
Ancient landslides with platform geomorphology occasionally reactivate,posing serious geohazards.On September 9,2021,persistent heavy rainfall triggered the reactivation of the Dahekou ancient landslide within a gentl...Ancient landslides with platform geomorphology occasionally reactivate,posing serious geohazards.On September 9,2021,persistent heavy rainfall triggered the reactivation of the Dahekou ancient landslide within a gently sloping geomorphology at the core of Zhangjiantan syncline in China's western Qinling-Daba Mountains.This event caused one death,damaged 80 houses,and blocked the Yushui River.This study reconstructs the sliding process of the Dahekou landslide and deciphers the complex landslide initiation mechanisms through field surveys,unmanned aerial vehicle(UAV)imagery analysis,drilling,electrical resistivity tomography(ERT)and small baseline subset–interferometric synthetic aperture radar(SBAS–InSAR)monitoring.We divide the sliding process of the Dahekou landslide into three stages.Two new landslides(#1 and#2)occurred at 18:30 on September 9,2021.Subsequently,the ancient landslide(#3)slid in the 230°direction at approximately 20:30 on September 9,2021,then changed the direction to 170°–240°at 22:30 on the same day,and moved in the direction of 300°at 10:00 the next day.Finally,the reactivated ancient landslide(#3)formed two partially sliding masses,with volumes of approximately 158×10^(4)m^(3)and 160×10~4 m^(3),along the directions of 170°–240°and 300°,respectively,damaging 80 houses and blocking the Yushui River.Field surveys suggest that new landslides#1 and#2 are rock landslides and soil landslides,respectively,with volumes of approximately 230×10^(4)m^(3)and 7.49×10~4 m^(3).Compared with the InSAR data,the new landslide#1 thrust the ancient landslide#3,with an uplift velocity rate of 22.68 mm/a at the rear edge,from September 2020–September 2021.An analysis of drill hole data reveals that the bedding in the landslide area has complex geological conditions,comprising mudstone prone to slipping with different degrees of weathering.Notably,the core of the Zhangjiatan syncline sits on the sliding bedding of the ancient landslide,contributing to a change in the sliding direction.This comprehensive study reveals that the landslide#1 loading and thrusting,the persistent and heavy rainfall,and the complex geological conditions influenced the reactivated ancient landslide.Considering the intricacies of landslide failure mechanisms,we advocate for giving more attention in the future to the zone of potentially slip-prone strata located at the edge of ancient landslides.展开更多
Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and char...Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and characterized by complex patterns, when the cost of repairing infrastructures, relocating communities, and restoring cultural sites might be such that it is unsustainable for the community, the exposed structures require significant effort for their surveillance and protection, which can be supported by the development of innovative monitoring systems. For this purpose, a smart extenso-inclinometer, realized by equipping a conventional inclinometer tube with distributed strain and temperature transducers based on optical fiber sensing technology, is presented. In situ monitoring of the active deep-seated San Nicola landslide in Centola (Campania, southern Italy) demonstrated its ability to capture the main features of movements and reconstruct a tridimensional evolution of the landslide pattern, even when the entity of both vertical and horizontal soil strain components is comparable. Although further tests are needed to definitively ascertain the extensometer function of the new device, by interpreting the strain profiles of the landslide body and identifying the achievement of predetermined thresholds, this system could provide a warning of the trigger of a landslide event. The use of the smart extenso-inclinometer within an early warning system for slow-moving landslides holds immense potential for reducing the impact of landslide events.展开更多
Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation...Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.展开更多
Economically and effectively managing the risk of landslide-generated impulse waves(LGIWs)presents a significant challenge following the impoundment of newly constructed reservoirs in western China.To address this iss...Economically and effectively managing the risk of landslide-generated impulse waves(LGIWs)presents a significant challenge following the impoundment of newly constructed reservoirs in western China.To address this issue,we selected the Wangjiashan(WJS)landslide in the Baihetan Reservoir area as a case study to evaluate LGIW hazards and develop corresponding mitigation strategies.Using 2D physical model tests and 3D numerical simulations,we established a 3D hazard assessment method for LGIWs based on 2D experimental results.This method confirmed the effectiveness of slope-cutting engineering in mitigating LGIW hazards.Based on this assessment framework,we proposed a novel approach for LGIW risk reduction.The results showed that the maximum wave amplitude reached 19.64 m in the Jinsha River channel,and the maximum run-up was 11.5 m in the XiangBiLing(XBL)community,indicating a substantial LGIW threat to the area.By reducing the rear edge of the sliding mass to 920 m above sea level(asl),the LGIW risk to the XBL community could be lowered to a tolerable level.Compared with traditional landslide prevention and control measures,the proposed mitigation scheme can reduce excavation costs by approximately 37 million CNY,making it a more scientifically sound and economically feasible solution.We explored the concept and the implementation of LGIW risk mitigation in depth,offering new insights for global LGIW risk management.This case study enhances our understanding of LGIW hazard prevention and provides valuable guidance for policymaking and engineering practices in similar geological settings worldwide.展开更多
Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological ...Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.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 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).
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”.
基金supported by the National Natural Science Foundation of China(42374019).
文摘Landslides accompanying earthquakes are essential in landscape evolution along active fault zones.However,most studies focus on the rapid,catastrophic coseismic landslides with surface scars;the role of slow-moving landslides and their relation with the coseismic landslides is poorly known.Combining radar interferometry,deep-learning network,and inventories of coseismic landslides,we show a clear complementary pattern between coseismic and slow-moving landslides distributed along the transition between the Qinghai-Xizang plateau and the Sichuan basin.Geomorphic analysis on areas dominated by coseismic and slow-moving landslides shows their distinct topographic fingerprints,suggesting that the coseismic landslides tend to occur on the top of the hill,while the slow-moving landslides erode the lower part of the slope.We infer that the coseismic landslides likely constrict the initiation and development of slow-moving landslides after large earthquakes by removing materials from slopes.Our results imply that the coseismic landslides may dominate the landscape feature close to active fault zones,where the lack of slow-moving landslides may indicate the historical occurrence of large-magnitude,landslide-prone earthquakes.
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supported by the National Natural Science Foundation of China(Grant No.U22A20596)the Shenzhen Science and Technology Program(Grant No.GJHZ20220913142605010)the Jinan Lead Researcher Project(Grant No.202333051).
文摘Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.
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 the MIUR-ex 60% Project(responsibility of Fabio Ietto)。
文摘Slow-moving landslides are widespread in the Mediterranean area,causing damage to the exposed facilities and economic losses in many countries.The recognition of slow-moving landslides in urban areas is always a difficult task to deal with because the presence of buildings,infrastructures,and human activities usually conceals the morphological signs of these landslide activities.So,in the last decades,numerous researchers have shown new methodologies to deepen the studies of similar instability phenomena.The present research is based on an integrated approach to investigate the landslide boundaries,type of movement,failure surface depth,and vulnerability state of buildings in Rota Greca Village(Calabria region,southern Italy) affected by a slowmoving landslide.For this purpose,multi-source data were acquired through geological and geomorphological surveys,recognition of landslide-induced damage on the built environment,subsurface investigations(e.g.,continuous drill boreholes,Standard Penetration Test,Rock Quality Designation index and inclinometer monitoring),laboratory tests(direct shear tests on undisturbed samples),geophysical survey,and InSAR-derived map of deformation rates.The complete integration of multi-source data allowed for the construction of reliable landslide modelling with relative geotechnical properties.In addition,the cross-comparison between surface deformation data by SAR images and severity damage level collected on the exposed buildings enabled to obtain the vulnerability map of the built area.In particular,the achieved goals highlighted two failure surfaces at about-13 and-25 m depth,causing a high vulnerability value for the buildings allocated in the central portion of the Rota Greca Village.The knowledge acquired by the multi-approach can be used to manage and implement appropriate landslide risk mitigation strategies,providing helpful advice and best practices to state-run organisations and stakeholders for landslide management in urban sites.
基金financially supported by the National Natural Science Foundation of China(Nos.42277149,41502299,41372306)the Research Planning of Sichuan Education Department,China(No.16ZB0105)+3 种基金the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Nos.SKLGP2016Z007,SKLGP2018Z017,SKLGP2020Z009)Chengdu University of Technology Young and Middle Aged Backbone Program(No.KYGG201720)Sichuan Provincial Science and Technology Department Program(No.19YYJC2087)China Scholarship Council。
文摘To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.
基金supported by the Major Program of the National Natural Science Foundation of China(No.42090054)the National Key Scientific Instrument and Equipment Development Projects of China(No.41827808)+1 种基金the Major Program of the National Natural Science Foundation of China(No.42090055)the National Science Foundation of China(No.42107194)。
文摘In the physical model test of landslides,the selection of analogous materials is the key,and it is difficult to consider the similarity of mechanical properties and seepage performance at the same time.To develop a model material suitable for analysing the deformation and failure of reservoir landslides,based on the existing research foundation of analogous materials,5 materials and 5 physical-mechanical parameters were selected to design an orthogonal test.The factor sensitivity of each component ratio and its influence on the physical-mechanical indices were studied by range analysis and stepwise regression analysis,and the proportioning method was determined.Finally,the model material was developed,and a model test was carried out considering Huangtupo as the prototype application.The results showed that(1)the model material composed of sand,barite powder,glass beads,clay,and bentonite had a wide distribution of physical-mechanical parameters,which could be applied to model tests under different conditions;(2)the physical-mechanical parameters of analogous materials matched the application prototype;and(3)the mechanical properties and seepage performance of the model material sample met the requirements of reservoir landslide model tests,which could be used to simulate landslide evolution and analyse the deformation process.
基金supported by the National Natural Science Foundation of China(Nos.42371094,41907253)the Fundamental Research Funds for the Central Universities(No.B250201054).
文摘0 INTRODUCTION Synthetic Aperture Radar(SAR)remote sensing,particularly with the C-band Sentinel-1 mission,has been widely used for landslide displacement analysis due to its high spatial resolution and revisit frequency(Zhou et al.,2024;Dai et al.,2021).However,in densely vegetated or humid mountainous regions such as the Three Gorges Reservoir(TGR),C-band signals suffer from temporal decorrelation,limiting their effectiveness for landslide monitoring.
文摘Landslides triggered by heavy rainfall pose a serious threat globally, endangering infrastructure and lives. Many previous landslide studies lack comprehensiveness and site specificity. Thus, a comprehensive investigation is essential to understand the failure mechanisms and contributing factors for assessing potential future hazards. This study aims to investigate the debris flow landslide that occurred in Kavalappara, Kerala, India, on August 8, 2019, through an integrated approach combining geophysical test, weathering characterization, geotechnical, and numerical analyses. Shear wave velocity(V_s) was determined using the Multi-Channel Analysis of Surface Waves(MASW) test to obtain the substrata of the slope. Residual and unsaturated soil properties were obtained through ring shear and dew point potentiometer tests. The mineralogical composition of the soil was identified using Field-Emission Scanning Electron Microscopy(FE-SEM), Energy Dispersive XRay Analysis(EDAX), and X-Ray Diffraction(XRD) patterns. These investigation results focused on slope stability during rainfall infiltration using Limit Equilibrium(LEM) and Finite Element Analysis(FEM) for both low and high-intensity rainfall. Finally, the progressive failure mechanism of the landslide was analysed using the Finite Difference program(FDM). The soil profile showed a variation from loose to dense, with a V_(s) range of 172.85 m/s to 440.53 m/s. No rock layers were identified down to a depth of 15 m. The landslide area consists of migmatite as a parent rock, and the soil was identified as silty clay, comprising quartz and clay minerals. The FEM and LEM analyses reveal that the factor of safety was reduced to 0.83 due to increased pore water pressure and the degree of saturation. The pore water pressure ratio(r_(u)), estimated at 0.32, was used in the FDM. The landslide, initiated at r_u of 0.35, reached maximum velocities of 15.4 m/s horizontally and 12.4 m/s vertically. This study helps disaster management to analyse debris flow and find effective mitigation strategies for hilly areas.
基金the Key Research and Development Project of Hubei Province(No.2021BCA219)。
文摘The mechanism involved in deep-seated landslide-debris flow disaster chains has been studied for many years,however,it is still not completely understood.This study aims to analyze the key factors that were involved and led to the geological disaster of Shaziba 62.0 m deep landslide-debris flow.Two extensive field investigations were conducted before and after the slope failure event.The study further used drilled cores,high-density resistivity method,and aerial photographs to obtain valuable insights into the disaster chain.It was found that opencast coal mining operations broke the locked segment of the front edge and heavy rainfall softened the slip zones along the faults.Mechanical calculations demonstrated that the coupling condition of the opencast coal mining and heavy rainfall triggered the landslide.A new evolution model was put forth to describe the complex mechanism of combining progressive retreat and tractive failure of hydraulic drive landslide,which was governed by the bedding-plane rock layer.Surface runoff caused the mass of the landslide to liquefy throughout the sliding process,resulting in overlapping deposits,debris-flow-barrier-lake,and erosion.These new insights led to the indication of a different triggering mechanism of landslides-debris flows,as well as laid the foundation for the proposed physical and mechanical mechanism model based on progressive retreat soil-rock mixed landslides with an upper locked segment and lower weak interlayer under heavy rainfall.
基金funded by the National Natural Science Foundation of China(Grant No.42077257)。
文摘Ancient landslides with platform geomorphology occasionally reactivate,posing serious geohazards.On September 9,2021,persistent heavy rainfall triggered the reactivation of the Dahekou ancient landslide within a gently sloping geomorphology at the core of Zhangjiantan syncline in China's western Qinling-Daba Mountains.This event caused one death,damaged 80 houses,and blocked the Yushui River.This study reconstructs the sliding process of the Dahekou landslide and deciphers the complex landslide initiation mechanisms through field surveys,unmanned aerial vehicle(UAV)imagery analysis,drilling,electrical resistivity tomography(ERT)and small baseline subset–interferometric synthetic aperture radar(SBAS–InSAR)monitoring.We divide the sliding process of the Dahekou landslide into three stages.Two new landslides(#1 and#2)occurred at 18:30 on September 9,2021.Subsequently,the ancient landslide(#3)slid in the 230°direction at approximately 20:30 on September 9,2021,then changed the direction to 170°–240°at 22:30 on the same day,and moved in the direction of 300°at 10:00 the next day.Finally,the reactivated ancient landslide(#3)formed two partially sliding masses,with volumes of approximately 158×10^(4)m^(3)and 160×10~4 m^(3),along the directions of 170°–240°and 300°,respectively,damaging 80 houses and blocking the Yushui River.Field surveys suggest that new landslides#1 and#2 are rock landslides and soil landslides,respectively,with volumes of approximately 230×10^(4)m^(3)and 7.49×10~4 m^(3).Compared with the InSAR data,the new landslide#1 thrust the ancient landslide#3,with an uplift velocity rate of 22.68 mm/a at the rear edge,from September 2020–September 2021.An analysis of drill hole data reveals that the bedding in the landslide area has complex geological conditions,comprising mudstone prone to slipping with different degrees of weathering.Notably,the core of the Zhangjiatan syncline sits on the sliding bedding of the ancient landslide,contributing to a change in the sliding direction.This comprehensive study reveals that the landslide#1 loading and thrusting,the persistent and heavy rainfall,and the complex geological conditions influenced the reactivated ancient landslide.Considering the intricacies of landslide failure mechanisms,we advocate for giving more attention in the future to the zone of potentially slip-prone strata located at the edge of ancient landslides.
基金supported by Universita della Campania“L.Vanvitelli”,Program VALERE“VAnviteLli pEr la RicErca”(Grant No.516/2018)Italian Ministry of Economic Development#NOACRONYM Project,PoC MISE 2021.
文摘Extensive urban areas worldwide face significant landslide hazards, impacting inhabitants, buildings, and critical infrastructures alike. In the case of slow-moving deep-seated landslides involving huge areas and characterized by complex patterns, when the cost of repairing infrastructures, relocating communities, and restoring cultural sites might be such that it is unsustainable for the community, the exposed structures require significant effort for their surveillance and protection, which can be supported by the development of innovative monitoring systems. For this purpose, a smart extenso-inclinometer, realized by equipping a conventional inclinometer tube with distributed strain and temperature transducers based on optical fiber sensing technology, is presented. In situ monitoring of the active deep-seated San Nicola landslide in Centola (Campania, southern Italy) demonstrated its ability to capture the main features of movements and reconstruct a tridimensional evolution of the landslide pattern, even when the entity of both vertical and horizontal soil strain components is comparable. Although further tests are needed to definitively ascertain the extensometer function of the new device, by interpreting the strain profiles of the landslide body and identifying the achievement of predetermined thresholds, this system could provide a warning of the trigger of a landslide event. The use of the smart extenso-inclinometer within an early warning system for slow-moving landslides holds immense potential for reducing the impact of landslide events.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225702)the Maria Skłodowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinable DevelopmEnt)project-HORIZON-MSCA-2022-SE-01(Grant No.101131146)。
文摘Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.
基金supported by the National Natural Science Foundation of China(No.U23A2045)the China Three Gorges Corporation(YM(BHT)/(22)022).
文摘Economically and effectively managing the risk of landslide-generated impulse waves(LGIWs)presents a significant challenge following the impoundment of newly constructed reservoirs in western China.To address this issue,we selected the Wangjiashan(WJS)landslide in the Baihetan Reservoir area as a case study to evaluate LGIW hazards and develop corresponding mitigation strategies.Using 2D physical model tests and 3D numerical simulations,we established a 3D hazard assessment method for LGIWs based on 2D experimental results.This method confirmed the effectiveness of slope-cutting engineering in mitigating LGIW hazards.Based on this assessment framework,we proposed a novel approach for LGIW risk reduction.The results showed that the maximum wave amplitude reached 19.64 m in the Jinsha River channel,and the maximum run-up was 11.5 m in the XiangBiLing(XBL)community,indicating a substantial LGIW threat to the area.By reducing the rear edge of the sliding mass to 920 m above sea level(asl),the LGIW risk to the XBL community could be lowered to a tolerable level.Compared with traditional landslide prevention and control measures,the proposed mitigation scheme can reduce excavation costs by approximately 37 million CNY,making it a more scientifically sound and economically feasible solution.We explored the concept and the implementation of LGIW risk mitigation in depth,offering new insights for global LGIW risk management.This case study enhances our understanding of LGIW hazard prevention and provides valuable guidance for policymaking and engineering practices in similar geological settings worldwide.
基金supported by Natural Science Research Project of Anhui Educational Committee(2023AH030041)National Natural Science Foundation of China(42277136)Anhui Province Young and Middle-aged Teacher Training Action Project(DTR2023018).
文摘Selection of negative samples significantly influences landslide susceptibility assessment,especially when establishing the relationship between landslides and environmental factors in regions with complex geological conditions.Traditional sampling strategies commonly used in landslide susceptibility models can lead to a misrepresentation of the distribution of negative samples,causing a deviation from actual geological conditions.This,in turn,negatively affects the discriminative ability and generalization performance of the models.To address this issue,we propose a novel approach for selecting negative samples to enhance the quality of machine learning models.We choose the Liangshan Yi Autonomous Prefecture,located in southwestern Sichuan,China,as the case study.This area,characterized by complex terrain,frequent tectonic activities,and steep slope erosion,experiences recurrent landslides,making it an ideal setting for validating our proposed method.We calculate the contribution values of environmental factors using the relief algorithm to construct the feature space,apply the Target Space Exteriorization Sampling(TSES)method to select negative samples,calculate landslide probability values by Random Forest(RF)modeling,and then create regional landslide susceptibility maps.We evaluate the performance of the RF model optimized by the Environmental Factor Selection-based TSES(EFSTSES)method using standard performance metrics.The results indicated that the model achieved an accuracy(ACC)of 0.962,precision(PRE)of 0.961,and an area under the curve(AUC)of 0.962.These findings demonstrate that the EFSTSES-based model effectively mitigates the negative sample imbalance issue,enhances the differentiation between landslide and non-landslide samples,and reduces misclassification,particularly in geologically complex areas.These improvements offer valuable insights for disaster prevention,land use planning,and risk mitigation strategies.
