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
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
After the Wenchuan earthquake on May 12, 2008 and the rainstorm and debris flow on August 14, 2010, the vegetation in Hongchun Gully, Yingxiu town is gradually recovering naturally, and the plant community is graduall...After the Wenchuan earthquake on May 12, 2008 and the rainstorm and debris flow on August 14, 2010, the vegetation in Hongchun Gully, Yingxiu town is gradually recovering naturally, and the plant community is gradually undergoing community succession. Based on the field vegetation survey data of 46 quadrats in 5 field plots, this study comprehensively analyzed the plant community, species number changes and vegetation characteristics of Hongchun Gully in Yingxiu town at different stages of vegetation succession after the earthquake by using the unified method. The results show that: 1) There are 61 families, 96 genera and 110 species of plants on the Hongchun Gully landslide. Among them, Cunninghamia lanceolata is the most widely distributed, and together with Cryptomeria japonica, it constitutes the main dominant species in the tree layer;The dominant species in shrub layer are Rubussp., Hydrangea strigosa, Rubus tephrodes, etc. The dominant species in herb layer are Artemisia argyi, Stellaria media and Senecio scandens. 2) The vegetation restoration in Hongchun Gully is relatively good, with herbs as the main species in the early stage of succession, shrubs and herbs as the main species in the middle stage of succession and trees as the main species in the late stage of succession;And the longer the succession time, the better the vegetation restoration and the richer the species. 3) Vegetation succession is related to the succession time, and the succession is always in the direction of strong adaptability. The study provides important data reference for further discussing the natural restoration and succession process and mechanism of plant communities on the damaged landslide formed after the earthquake, and provides theoretical basis for vegetation restoration and ecological reconstruction in the hardest hit areas in southwest China after the earthquake.展开更多
The article presents the results of field, office and laboratory research of landslide areas developed on the right slope of the Zhinvali Reservoir section of the Georgian Military Road, according to which the area of...The article presents the results of field, office and laboratory research of landslide areas developed on the right slope of the Zhinvali Reservoir section of the Georgian Military Road, according to which the area of spread of these events, the dynamics of development, the threats arising from them in terms of restricting traffic, and a plan for developing landslide prevention measures are recorded. The underlying rocks of the slopes have been studied, cross-sections have been created, their types and physical and mechanical characteristics have been determined, and the natural and anthropogenic (technogenic) factors that contribute to the origin and development of the aforementioned phenomena have been identified. Based on the results of the study, it can be said that landslide areas developed along the transportation lane are mostly associated with erosion processes developing in small ravines, where the rocks forming the slope are highly eroded and disintegrated. Unregulated surface water runoff has a great impact on their development. There are frequent cases when the landslide dynamics develop in a reverse direction, gradually covering the upper layers of the slope and closely approaching the roadway. Based on desk work and laboratory data, a numerical calculation of the stability conditions (K coefficient) was carried out for typical landslide sites, considering natural factors (seismic events and increased rock moisture). Based on the data obtained as a result of the research, it became possible to develop various types of landslide prevention measures.展开更多
Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatia...Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).展开更多
The creep-slip behavior of creeping landslides is closely related to the creep characteristics of slope rock.This study analyzed the creep behavior of ultra-soft mudstone from the Gaomiao landslide in Haidong City,Qin...The creep-slip behavior of creeping landslides is closely related to the creep characteristics of slope rock.This study analyzed the creep behavior of ultra-soft mudstone from the Gaomiao landslide in Haidong City,Qinghai Province,China.Uniaxial creep tests were carried out on ultra-soft mudstone with various moisture contents.The test results indicated that the creep duration of the rock sample with a natural moisture content of 9%is 2400 times longer than that of the sample with a natural moisture content of 13%,while its accumulated strain is 70%of the latter.For the rock sample with a natural moisture content of 9.80%,the creep duration under 0.5 MPa load is 80%of that under 0.25 MPa load,yet the accumulated strain is 1.4 times greater.Additionally,porosity significantly influences the creep behavior of mudstone.Analysis of the cause of the Gaomiao landslide and field monitoring data indicates that the instability of the Gaomiao landslide is related to the moisture content of the landslip mass and external forces.The creep-slip curves of landslides and the creep deformation curves of rocks share a common trend.Precisely identifying the moment when the shift occurs from steady state creep to accelerated creep is critical for comprehending slope instability and rock failure.Moreover,this study delves deeper into the issue of the consistency between landslide creep and rock deformation.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
Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the sta...Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the stability of landslide dams and the potential for dam breaches.To explore the failure mechanisms and evolutionary processes of landslide dams with varying soil properties,this study conducted a series of flume experiments,considering different grain compositions and material densities.The results demonstrated that grading characteristics significantly influence landslide dam stability,affecting failure patterns,breach processes,and final breach morphologies.Fine-graded materials exhibited a sequence of surface erosion,head-cut erosion,and subsequent surface erosion during the breach process,while well-graded materials typically experienced head-cut erosion followed by surface erosion.In coarse-graded dams,the high permeability of coarse particles allowed the dam to remain stable,as inflows matched outflows.The dam breach model experiments also showed that increasing material density effectively delayed the breach and reduced peak breach flow discharge.Furthermore,higher fine particle content led to a reduction in the residual dam height and the base slope of the final breach profile,although the relationship between peak breach discharge and the content of fine and coarse particles was nonlinear.To better understand breach morphology evolution under different soil characteristics and hydraulic conditions,three key points were identified—erosion point,control point,and scouring point.This study,by examining the evolution of failure patterns,breach processes,and breach flow discharges under various grading and density conditions,offers valuable insights into the mechanisms behind landslide dam failures.展开更多
This paper presents a dynamic modeling method to test and examine the minimum mass of pressurized pore-gas for triggering landslides in stable gentle soil slopes.A stable gentle soil slope model is constructed with a ...This paper presents a dynamic modeling method to test and examine the minimum mass of pressurized pore-gas for triggering landslides in stable gentle soil slopes.A stable gentle soil slope model is constructed with a dry cement powder core,a saturated clay middle layer,and a dry sand upper layer.The test injects H_(2)O_(2)solution into the cement core to produce new pore-gas.The model test includes three identical H_(2)O_(2)injections.The small mass of generated oxygen gas(0.07%of slope soil mass and landslide body)from the first injection can build sufficient pore-gas pressure to cause soil upheaval and slide.Meanwhile,despite the first injection causing leak paths in the clay layer,the generated small mass of gas from the second and third injections can further trigger the landslide.A dynamic theoretical analysis of the slope failure is carried out and the required minimum pore-gas pressure for the landslide is calculated.The mass and pressure of generated gas in the model test are also estimated based on the calibration test for oxygen generation from H_(2)O_(2)solution in cement powder.The results indicate that the minimum mass of the generated gas for triggering the landslide is 2 ppm to 0.07%of the landslide body.Furthermore,the small mass of gas can provide sufficient pressure to cause soil upheaval and soil sliding in dynamic analysis.展开更多
Loess-mudstone landslides are common in the Loess Plateau.Investigations into the mechanical theory of loess-mudstone landslides have become a challenging undertaking due to the distinctive interfacial properties of l...Loess-mudstone landslides are common in the Loess Plateau.Investigations into the mechanical theory of loess-mudstone landslides have become a challenging undertaking due to the distinctive interfacial properties of loess-mudstone and the unique water sensitivity characteristics of mudstone.Hence,it is imperative to develop innovative mechanical models and mathematical equations specifically tailored to loess-mudstone landslides.In this study,we analyze the fracture mechanism of the loess-mudstone sliding zone using plastic fracture mechanics and develop a unique fracture yield model.To calculate the energy release rate during the expansion of the loess-mudstone interface tip region,the shear fracture energy G is applied,which reflects both the yield failure criterion and the fracture failure criterion.To better understand the instability mechanism of loess-mudstone landslides,equilibrium equations based on G are established for tractive,compressive,and tensile loess-mudstone landslides.Based on the equilibrium equation,the critical length Lc of the sliding zone can be used for the safety evaluation of loess-mudstone landslides.In this way,this study proposes a new method for determining the failure mechanism and equilibrium equation of loessmudstone landslides,which resolves their starting mechanism,mechanical equilibrium equations,and safety evaluation indicators,thus justifying the scientific significance and practical value of this research.展开更多
Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explo...Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.展开更多
The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been l...The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected.This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area(TGRA)as a case study.We collected surface automatic monitoring data at different intervals,ranging from daily to monthly.The Ensemble Empirical Mode Decomposition(EEMD)algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency.Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models:Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU).The predictions from these models were integrated to derive cumulative displacement forecasts,enabling a comparative analysis of prediction accuracy across different monitoring frequencies.The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting,with optimal performance observed at moderate monitoring intervals.Intriguingly,the daily mean average error(MAE)decreases sharply with increasing monitoring frequency,reaching a plateau.These findings were corroborated by a parallel analysis of the Bazimen landslide,suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.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
Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms ...Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms that affect farmers'willingness to participate in landslide prevention and mitigation(WPLPM).Conducted in Heifangtai,Gansu Province,China,renowned as the"landslide natural laboratory",this research employs multiple linear regression analysis on data from 399 questionnaires to pinpoint the key determinants of farmers'WPLPM.The findings reveal:(1)the"risk perception paradox"exists—farmers have high-risk perception but low WPLPM;(2)the impact of risk perception on WPLPM is tempered by self-efficacy related to fund,learning ability,and operation ability,offering an insight into the"risk perception paradox";and(3)There are significant positive influences of farmers'benefit perception,social network,and perceived responsibility on their WPLPM.Based on these insights,the study offers targeted policy recommendations.展开更多
Understanding the stress distribution derived from monitoring the principal stress(PS)in slopes is of great importance.In this study,a miniature sensor for quantifying the two-dimensional(2D)PS in landslide model test...Understanding the stress distribution derived from monitoring the principal stress(PS)in slopes is of great importance.In this study,a miniature sensor for quantifying the two-dimensional(2D)PS in landslide model tests is proposed.The fundamental principle and design of the sensor are demonstrated.The sensor comprises three earth pressure gages and one gyroscope,with the utilization of three-dimensional(3D)printing technology.The difficulties of installation location during model preparation and sensor rotation during testing can be effectively overcome using this sensor.Two different arrangements of the sensors are tested in verification tests.Additionally,the application of the sensor in an excavated-induced slope model is tested.The results demonstrate that the sensor exhibits commendable performance and achieves a desirable level of accuracy,with a principal stress angle error of±5°in the verification tests.The stress transformation of the slope model,generated by excavation,is demonstrated in the application test by monitoring the two miniature principal stress(MPS)sensors.The sensor has a significant potential for measuring primary stress in landslide model tests and other geotechnical model experiments.展开更多
Earthquake-induced landslides have always been a hot research topic in the field of geosciences.However,there have been few bibliometric analyses on this topic.To systematically understand the research status,this stu...Earthquake-induced landslides have always been a hot research topic in the field of geosciences.However,there have been few bibliometric analyses on this topic.To systematically understand the research status,this study is based on bibliometrics and extensively uses visualization analysis techniques.It combines quantitative and qualitative methods to conduct an in-depth analysis of 5016 papers collected from the Web of Science(www.webofscience.com).The results revealed that:(1)The number of papers on earthquake-induced landslides is steadily increasing,and is expected to continue to rise.(2)Countries prone to frequent earthquakes have made significant contributions to the research on earthquake-induced landslides,and the frequent and effective cooperation among these countries has had a very positive impact on promoting landslide study.(3)Research on earthquake-induced landslides is no longer limited to the field of geology,and the future direction is to integrate knowledge and technical methods from multiple disciplines.In the research methods of earthquake-induced landslides,there is a gradual shift from"experience,theory"to"data-driven".This study can provide researchers in this field with information on the core research forces,evolving hot topics,and future development trends of earthquake-induced landslides.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
The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide dist...The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.展开更多
Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,...Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.展开更多
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.
文摘After the Wenchuan earthquake on May 12, 2008 and the rainstorm and debris flow on August 14, 2010, the vegetation in Hongchun Gully, Yingxiu town is gradually recovering naturally, and the plant community is gradually undergoing community succession. Based on the field vegetation survey data of 46 quadrats in 5 field plots, this study comprehensively analyzed the plant community, species number changes and vegetation characteristics of Hongchun Gully in Yingxiu town at different stages of vegetation succession after the earthquake by using the unified method. The results show that: 1) There are 61 families, 96 genera and 110 species of plants on the Hongchun Gully landslide. Among them, Cunninghamia lanceolata is the most widely distributed, and together with Cryptomeria japonica, it constitutes the main dominant species in the tree layer;The dominant species in shrub layer are Rubussp., Hydrangea strigosa, Rubus tephrodes, etc. The dominant species in herb layer are Artemisia argyi, Stellaria media and Senecio scandens. 2) The vegetation restoration in Hongchun Gully is relatively good, with herbs as the main species in the early stage of succession, shrubs and herbs as the main species in the middle stage of succession and trees as the main species in the late stage of succession;And the longer the succession time, the better the vegetation restoration and the richer the species. 3) Vegetation succession is related to the succession time, and the succession is always in the direction of strong adaptability. The study provides important data reference for further discussing the natural restoration and succession process and mechanism of plant communities on the damaged landslide formed after the earthquake, and provides theoretical basis for vegetation restoration and ecological reconstruction in the hardest hit areas in southwest China after the earthquake.
文摘The article presents the results of field, office and laboratory research of landslide areas developed on the right slope of the Zhinvali Reservoir section of the Georgian Military Road, according to which the area of spread of these events, the dynamics of development, the threats arising from them in terms of restricting traffic, and a plan for developing landslide prevention measures are recorded. The underlying rocks of the slopes have been studied, cross-sections have been created, their types and physical and mechanical characteristics have been determined, and the natural and anthropogenic (technogenic) factors that contribute to the origin and development of the aforementioned phenomena have been identified. Based on the results of the study, it can be said that landslide areas developed along the transportation lane are mostly associated with erosion processes developing in small ravines, where the rocks forming the slope are highly eroded and disintegrated. Unregulated surface water runoff has a great impact on their development. There are frequent cases when the landslide dynamics develop in a reverse direction, gradually covering the upper layers of the slope and closely approaching the roadway. Based on desk work and laboratory data, a numerical calculation of the stability conditions (K coefficient) was carried out for typical landslide sites, considering natural factors (seismic events and increased rock moisture). Based on the data obtained as a result of the research, it became possible to develop various types of landslide prevention measures.
文摘Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).
基金The work described in this paper is partially supported by the Second Tibetan Plateau Scientific Expedition and Research Grant(2019QZKK0708)ARC Discovery Project grants(DP210100437,DP230100126),for which the authors are very grateful.
文摘The creep-slip behavior of creeping landslides is closely related to the creep characteristics of slope rock.This study analyzed the creep behavior of ultra-soft mudstone from the Gaomiao landslide in Haidong City,Qinghai Province,China.Uniaxial creep tests were carried out on ultra-soft mudstone with various moisture contents.The test results indicated that the creep duration of the rock sample with a natural moisture content of 9%is 2400 times longer than that of the sample with a natural moisture content of 13%,while its accumulated strain is 70%of the latter.For the rock sample with a natural moisture content of 9.80%,the creep duration under 0.5 MPa load is 80%of that under 0.25 MPa load,yet the accumulated strain is 1.4 times greater.Additionally,porosity significantly influences the creep behavior of mudstone.Analysis of the cause of the Gaomiao landslide and field monitoring data indicates that the instability of the Gaomiao landslide is related to the moisture content of the landslip mass and external forces.The creep-slip curves of landslides and the creep deformation curves of rocks share a common trend.Precisely identifying the moment when the shift occurs from steady state creep to accelerated creep is critical for comprehending slope instability and rock failure.Moreover,this study delves deeper into the issue of the consistency between landslide creep and rock deformation.
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financially supported by the National Natural Science Foundation of China(Grant Nos.U22A20602,U2040221,and 42207228)the Sichuan Science and Technology Program(2022NSFSC1060)the Fundamental Research Funds for Central Public Research Institutes(Grant No.Y324006)。
文摘Landslide dams,as frequent natural hazards,pose significant risks to human lives,property,and ecological environments.The grading characteristics and density of dam materials play a crucial role in determining the stability of landslide dams and the potential for dam breaches.To explore the failure mechanisms and evolutionary processes of landslide dams with varying soil properties,this study conducted a series of flume experiments,considering different grain compositions and material densities.The results demonstrated that grading characteristics significantly influence landslide dam stability,affecting failure patterns,breach processes,and final breach morphologies.Fine-graded materials exhibited a sequence of surface erosion,head-cut erosion,and subsequent surface erosion during the breach process,while well-graded materials typically experienced head-cut erosion followed by surface erosion.In coarse-graded dams,the high permeability of coarse particles allowed the dam to remain stable,as inflows matched outflows.The dam breach model experiments also showed that increasing material density effectively delayed the breach and reduced peak breach flow discharge.Furthermore,higher fine particle content led to a reduction in the residual dam height and the base slope of the final breach profile,although the relationship between peak breach discharge and the content of fine and coarse particles was nonlinear.To better understand breach morphology evolution under different soil characteristics and hydraulic conditions,three key points were identified—erosion point,control point,and scouring point.This study,by examining the evolution of failure patterns,breach processes,and breach flow discharges under various grading and density conditions,offers valuable insights into the mechanisms behind landslide dam failures.
基金supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region,China(Project No.HKU 17207518).
文摘This paper presents a dynamic modeling method to test and examine the minimum mass of pressurized pore-gas for triggering landslides in stable gentle soil slopes.A stable gentle soil slope model is constructed with a dry cement powder core,a saturated clay middle layer,and a dry sand upper layer.The test injects H_(2)O_(2)solution into the cement core to produce new pore-gas.The model test includes three identical H_(2)O_(2)injections.The small mass of generated oxygen gas(0.07%of slope soil mass and landslide body)from the first injection can build sufficient pore-gas pressure to cause soil upheaval and slide.Meanwhile,despite the first injection causing leak paths in the clay layer,the generated small mass of gas from the second and third injections can further trigger the landslide.A dynamic theoretical analysis of the slope failure is carried out and the required minimum pore-gas pressure for the landslide is calculated.The mass and pressure of generated gas in the model test are also estimated based on the calibration test for oxygen generation from H_(2)O_(2)solution in cement powder.The results indicate that the minimum mass of the generated gas for triggering the landslide is 2 ppm to 0.07%of the landslide body.Furthermore,the small mass of gas can provide sufficient pressure to cause soil upheaval and soil sliding in dynamic analysis.
基金supported by The National Natural Science Foundation of China(Grant No.12362034)The Scientific Research Project of Inner Mongolia University of Technology(Grant Nos.DC2200000913+1 种基金DC2300001439)The Science and Technology Plan Project of Inner Mongolia Autonomous Region(Grant No.2022YFSH0047)。
文摘Loess-mudstone landslides are common in the Loess Plateau.Investigations into the mechanical theory of loess-mudstone landslides have become a challenging undertaking due to the distinctive interfacial properties of loess-mudstone and the unique water sensitivity characteristics of mudstone.Hence,it is imperative to develop innovative mechanical models and mathematical equations specifically tailored to loess-mudstone landslides.In this study,we analyze the fracture mechanism of the loess-mudstone sliding zone using plastic fracture mechanics and develop a unique fracture yield model.To calculate the energy release rate during the expansion of the loess-mudstone interface tip region,the shear fracture energy G is applied,which reflects both the yield failure criterion and the fracture failure criterion.To better understand the instability mechanism of loess-mudstone landslides,equilibrium equations based on G are established for tractive,compressive,and tensile loess-mudstone landslides.Based on the equilibrium equation,the critical length Lc of the sliding zone can be used for the safety evaluation of loess-mudstone landslides.In this way,this study proposes a new method for determining the failure mechanism and equilibrium equation of loessmudstone landslides,which resolves their starting mechanism,mechanical equilibrium equations,and safety evaluation indicators,thus justifying the scientific significance and practical value of this research.
基金Jiangxi Provincial Innovative Training Project“Post-earthquake Landslide Risk Evaluation Study under Spatial Scale Modelling”(Project No.:S202311318050)。
文摘Landslides are significant natural geological hazards.Landslide susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their probabilities.Research has explored susceptibility assessment methods based on spatial-scale analysis.This evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled model.Key aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.
基金supported by the Open Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(China Three Gorges University)of the Ministry of Education(Grant Nos.2022KDZ14 and 2022KDZ15)the Open Fund of Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202304)+3 种基金the Science and Technology Project of Department of Natural Resources of Hubei Province(Grant No.ZRZY2024KJ15)the Natural Science Foundation of Hubei Province(Grant No.2022CFB557)the National Natural Science Foundation of China(Grant No.42107489)the 111 Project of Hubei Province(Grant No.2021EJD026)。
文摘The precision of landslide displacement prediction is crucial for effective landslide prevention and mitigation strategies.However,the role of surface monitoring frequency in influencing prediction accuracy has been largely neglected.This study examined the effect of varying monitoring frequencies on the accuracy of displacement predictions by using the Baijiabao landslide in the Three Gorges Reservoir Area(TGRA)as a case study.We collected surface automatic monitoring data at different intervals,ranging from daily to monthly.The Ensemble Empirical Mode Decomposition(EEMD)algorithm was utilized to dissect the accumulated displacements into periodic and trend components at each monitoring frequency.Polynomial fitting was applied to forecast the trend component while the periodic component was predicted with two state-of-the-art neural network models:Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU).The predictions from these models were integrated to derive cumulative displacement forecasts,enabling a comparative analysis of prediction accuracy across different monitoring frequencies.The results demonstrate that the proposed models achieve high accuracy in landslide displacement forecasting,with optimal performance observed at moderate monitoring intervals.Intriguingly,the daily mean average error(MAE)decreases sharply with increasing monitoring frequency,reaching a plateau.These findings were corroborated by a parallel analysis of the Bazimen landslide,suggesting that moderate monitoring intervals of approximately 7 to 15 days are most conducive to achieving enhanced prediction accuracy compared to both daily and monthly intervals.
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funded by National Social Science Foundation of China(Grant Number 24&ZD164)。
文摘Public participation is crucial in mitigating disasters.Stemming from the ongoing debate on benefit-and risk-driven approaches to landslide mitigation,this study seeks to uncover the factors and underlying mechanisms that affect farmers'willingness to participate in landslide prevention and mitigation(WPLPM).Conducted in Heifangtai,Gansu Province,China,renowned as the"landslide natural laboratory",this research employs multiple linear regression analysis on data from 399 questionnaires to pinpoint the key determinants of farmers'WPLPM.The findings reveal:(1)the"risk perception paradox"exists—farmers have high-risk perception but low WPLPM;(2)the impact of risk perception on WPLPM is tempered by self-efficacy related to fund,learning ability,and operation ability,offering an insight into the"risk perception paradox";and(3)There are significant positive influences of farmers'benefit perception,social network,and perceived responsibility on their WPLPM.Based on these insights,the study offers targeted policy recommendations.
基金supported by the National Nature Science Foundation of China(Grant No.42207216)the Major Program of the National Natural Science Foundation of China(Grant No.42090055)the National Nature Science Foundation of China(Grant No.42377182).
文摘Understanding the stress distribution derived from monitoring the principal stress(PS)in slopes is of great importance.In this study,a miniature sensor for quantifying the two-dimensional(2D)PS in landslide model tests is proposed.The fundamental principle and design of the sensor are demonstrated.The sensor comprises three earth pressure gages and one gyroscope,with the utilization of three-dimensional(3D)printing technology.The difficulties of installation location during model preparation and sensor rotation during testing can be effectively overcome using this sensor.Two different arrangements of the sensors are tested in verification tests.Additionally,the application of the sensor in an excavated-induced slope model is tested.The results demonstrate that the sensor exhibits commendable performance and achieves a desirable level of accuracy,with a principal stress angle error of±5°in the verification tests.The stress transformation of the slope model,generated by excavation,is demonstrated in the application test by monitoring the two miniature principal stress(MPS)sensors.The sensor has a significant potential for measuring primary stress in landslide model tests and other geotechnical model experiments.
基金supported by the National Natural Science Foundation of China(No.41931296)National Key R&D Program of China(No.2023YFC3007100)Tianfu Yongxing Laboratory Organized Research Project Funding(No.2023KJGG05)。
文摘Earthquake-induced landslides have always been a hot research topic in the field of geosciences.However,there have been few bibliometric analyses on this topic.To systematically understand the research status,this study is based on bibliometrics and extensively uses visualization analysis techniques.It combines quantitative and qualitative methods to conduct an in-depth analysis of 5016 papers collected from the Web of Science(www.webofscience.com).The results revealed that:(1)The number of papers on earthquake-induced landslides is steadily increasing,and is expected to continue to rise.(2)Countries prone to frequent earthquakes have made significant contributions to the research on earthquake-induced landslides,and the frequent and effective cooperation among these countries has had a very positive impact on promoting landslide study.(3)Research on earthquake-induced landslides is no longer limited to the field of geology,and the future direction is to integrate knowledge and technical methods from multiple disciplines.In the research methods of earthquake-induced landslides,there is a gradual shift from"experience,theory"to"data-driven".This study can provide researchers in this field with information on the core research forces,evolving hot topics,and future development trends of earthquake-induced landslides.
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CSIR for providing financial assistance(09/0420(11800)/2021EMR-I)。
文摘The Kumaun Himalaya is well-known as a geologically and tectonically complex region that amplifies mass wasting processes,particularly landslides.This study attempts to investigate the interplay between landslide distribution and the lithotectonic regime of Darma Valley,Kumaun Himalaya.A landslide inventory comprising 295 landslides in the area has been prepared and several morphotectonic proxies such as valley floor width to height ratio(Vf),stream length gradient index(SL),and hypsometric integral(HI)have been used to infer tectonic regime.Morphometric analysis,including basic,linear,aerial,and relief aspects,of 59 fourth-order sub-basins,has been carried out to estimate erosion potential in the study area.The result demonstrates that 46.77%of the landslides lie in very high,20.32%in high,21.29%in medium,and 11.61%in low erosion potential zones respectively.In order to determine the key parameters controlling erosion potential,two multivariate statistical methods namely Principal Component Analysis(PCA)and Agglomerative Hierarchical Clustering(AHC)were utilized.PCA reveals that the Higher Himalayan Zone(HHZ)has the highest erosion potential due to the presence of elongated sub-basins characterized by steep slopes and high relief.The clusters created through AHC exhibit positive PCA values,indicating a robust correlation between PCA and AHC.Furthermore,the landslide density map shows two major landslide hotspots.One of these hotspots lies in the vicinity of highly active Munsiyari Thrust(MT),while the other is in the Pandukeshwar formation within the MT's hanging wall,characterized by a high exhumation rate.High SL and low Vf values along these hotspots further corroborate that the occurrence of landslides in the study area is influenced by tectonic activity.This study,by identifying erosionprone areas and elucidating the implications of tectonic activity on landslide distribution,empowers policymakers and government agencies to develop strategies for hazard assessment and effective landslide risk mitigation,consequently safeguarding lives and communities.
基金funded by the So Lo Mon project“Monitoraggio a Lungo Termine di Grandi Frane basato su Sistemi Integrati di Sensori e Reti”(Longterm monitoring of large-scale landslides based on integrated systems of sensors and networks),Program EFRE-FESR 2014–2020,Project EFRE-FESR4008 South Tyrol–Person in charge:V.Mair。
文摘Large-scale deep-seated landslides pose a significant threat to human life and infrastructure.Therefore,closely monitoring these landslides is crucial for assessing and mitigating their associated risks.In this paper,the authors introduce the So Lo Mon framework,a comprehensive monitoring system developed for three large-scale landslides in the Autonomous Province of Bolzano,Italy.A web-based platform integrates various monitoring data(GNSS,topographic data,in-place inclinometer),providing a user-friendly interface for visualizing and analyzing the collected data.This facilitates the identification of trends and patterns in landslide behaviour,enabling the triggering of warnings and the implementation of appropriate mitigation measures.The So Lo Mon platform has proven to be an invaluable tool for managing the risks associated with large-scale landslides through non-structural measures and driving countermeasure works design.It serves as a centralized data repository,offering visualization and analysis tools.This information empowers decisionmakers to make informed choices regarding risk mitigation,ultimately ensuring the safety of communities and infrastructures.
