A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in vari...A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.展开更多
Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard re...Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard require the accurate prediction of near-field wave characteristics,such as wave amplitude and run-up.However,near-field LGW involves complicated fluid-solid interactions.Furthermore,the wave characteristics are closely related to various aspects,including the geometry and physical features of the slide,river,and body of water.However,the empirical or analytical methods used for rough estimation cannot derive accurate results,especially for deformable landslides,due to their significant geometry changes during the sliding process.In this study,the near-field waves generated by deformable landslides were simulated by smoothed particle hydrodynamics(SPH)based on multi-phase flow.The deformable landslides were generalized as a kind of viscous flow by adopting the Herschel-Bulkley-Papanastasiou(HBP)-based nonNewtonian rheology model.The HBP model is capable of producing deformable landslide dynamics even though the high-speed sliding process is involved.In this study,an idealized experiment case originating from Lituya LGW and a practical case of Gongjiafang LGW were reproduced for verification and demonstration.The simulation results of both cases show satisfactory consistency with the experiment/investigation data in terms of landslide movement and near-field impulsive wave characteristics,thus indicating the applicability and accuracy of the proposed method.Finally,the effects of the HBP model’s rheological parameters on the landslide dynamics and near-field wave characteristics are discussed,providing a parameter calibration method along with sug-gestions for further applications.展开更多
The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenz...The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.展开更多
A shaking table test for a bridge foundation reinforced by anti-slide piles on a silty clay landslide model with an inclined interlayer was performed.The deformation characteristics of the bridge foundation piles and ...A shaking table test for a bridge foundation reinforced by anti-slide piles on a silty clay landslide model with an inclined interlayer was performed.The deformation characteristics of the bridge foundation piles and anti-slide piles were analyzed in different loading conditions.The dynamic response law of a silty clay landslide with an inclined interlayer was summarized.The spacing between the rear anti-slide piles and bridge foundation should be reasonably controlled according to the seismic fortification requirements,to avoid the two peaks in the forced deformation of the bridge foundation piles.The“blocking effect”of the bridge foundation piles reduced the deformation of the forward anti-slide piles.The stress-strain response of silty clay was intensified as the vibration wave field appeared on the slope.Since the vibration intensified,the thrust distribution of the landslide underwent a process of shifting from triangle to inverted trapezoid,the difference in the acceleration response between the bearing platform and silty clay landslide tended to decrease,and the spectrum amplitude near the natural vibration frequency increased.The rear anti-slide piles were able to slow down the shear deformation of the soil in front of the piles and avoid excessive acceleration response of the bridge foundation piles.展开更多
Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods...Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.展开更多
Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calcu...Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond th...Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.展开更多
On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage ...On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.展开更多
Geomorphological features are commonly used to identify potential landslides.Nevertheless,overemphasis on these features could lead to misjudgment.This research proposes a process-oriented approach for potential lands...Geomorphological features are commonly used to identify potential landslides.Nevertheless,overemphasis on these features could lead to misjudgment.This research proposes a process-oriented approach for potential landslide identification that considers time-dependent behaviors.The method integrates comprehensive remote sensing and geological analysis to qualitatively assess slope stability,and employs numerical analysis to quantitatively calculate aging stability.Specifically,a time-dependent stability calculation method for anticlinal slopes is developed and implemented in discrete element software,incorporating time-dependent mechanical and strength reduction calculations.By considering the time-dependent evolution of slopes,this method highlights the importance of both geomorphological features and time-dependent behaviors in landslide identification.This method has been applied to the Jiarishan slope(JRS)on the Qinghai-Tibet Plateau as a case study.The results show that the JRS,despite having landslide geomorphology,is a stable slope,highlighting the risk of misjudgment when relying solely on geomorphological features.This work provides insights into the geomorphological characterization and evolution history of the JRS and offers valuable guidance for studying slopes with similar landslide geomorphology.Furthermore,the process-oriented method incorporating timedependent evolution provides a means to evaluate potential landslides,reducing misjudgment due to excessive reliance on geomorphological features.展开更多
Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Co...Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.展开更多
Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbaniz...Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.展开更多
The southeastern margin of Qinghai-Tibet Plateau(SMQTP)is of a typical large landslide-prone area due to intense tectonic activity,deeply incised valleys,high geostress and frequent earthquakes.To gain insights into l...The southeastern margin of Qinghai-Tibet Plateau(SMQTP)is of a typical large landslide-prone area due to intense tectonic activity,deeply incised valleys,high geostress and frequent earthquakes.To gain insights into large landslides in southeastern margin of Qinghai-Tibet Plateau,an area covering 3.34×105 km2 that extends 80e150 km on both sides of the Sichuan-Tibet traffic corridors(G318)was used to examine the spatial distribution and corresponding characteristics of landslides.The results showed that the study area contains at least 629 large landslides that are mainly concentrated on 7 zones(zones IeVII).Zones IeVII are in the southern section of the Longmenshan fault zone(with no large river)and sections with Dadu River,Jinsha River,Lancang River,Nujiang River and Yarlung Zangbo River.There are more landslides in the Jinsha River section(totaling 186 landslides)than the other sections.According to the updated Varnes classification,408 large landslides(64.9%)were recognized and divided into 4 major types,i.e.flows(275 cases),slides(58 cases),topples(44 cases)and slope deformations(31 cases).Flows,which consist of rock avalanches and iceerock avalanches,are the most common landslide type.Large landslide triggers(178 events,28.3%)are also recognized,and earthquakes may be the most common trigger.Due to the limited data,these landslide type classifications and landslide triggers are perhaps immature,and further systematic analysis is needed.展开更多
Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects...Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.展开更多
There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement an...There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.展开更多
After the impoundment of the Three Gorges Reservoir,some huge ancient landslides were reactivated and deformed,showing typical hydrodynamic pressure landslide characteristics.The Baishuihe landslide was a typical hydr...After the impoundment of the Three Gorges Reservoir,some huge ancient landslides were reactivated and deformed,showing typical hydrodynamic pressure landslide characteristics.The Baishuihe landslide was a typical hydrodynamic pressure landslide.The management department conducted slope cutting treatments from 2018 to 2019.To evaluate the treatment effect of rear slope cutting,this study analyzed the data of the surface deformation survey and field monitoring over the past 20 years and the characteristics of the reservoir water-triggered Baishuihe landslide deformation,and calculated the seepage field,displacement field,and stability coefficient before and after landslide treatment.The results showed that the deformation of the Baishuihe landslide was primarily related to a decrease in the reservoir water level.Owing to the poor permeability of the landslide soil,the decrease in the reservoir water level produced a seepage force pointing to the outside of the landslide body,leading to the step deformation of the landslide displacement.The landslide was treated by rear slope cutting,and the“step”deformation of the landslide disappeared after treatment.The hydrodynamic pressure caused by the change in reservoir water after cutting the slope did not disappear.However,as the slope cutting greatly reduced the overall sliding force of the landslide,its stability was greatly improved.Notably,high stability can still be ensured under extreme rainfall after treatment.Slope cutting is effective for treating hydrodynamic pressure landslides.This study can provide effective technical support for the treatment of reservoir landslides.展开更多
Landslides in the Himalayan region are primarily controlled by natural parameters,including rainfall,seismic activity,and anthropogenic parameters,such as the construction of large-scale projects like road development...Landslides in the Himalayan region are primarily controlled by natural parameters,including rainfall,seismic activity,and anthropogenic parameters,such as the construction of large-scale projects like road development,tunneling and hydroelectric power projects and climate change.The parameters which are more crucial among these are a matter of scientific study and analysis.This research,taking Solan district,Himachal Pradesh,India,as the study area,aims to assess the impact of anthropogenic activities on landslide susceptibility at a regional scale.Landslide distribution was characterized into two groups,namely Rainfall-Induced Landslide(RIL)and Human-Induced Landslide(HIL)based on triggering factors.Multiple data such as slope angle,aspect,profile curvature,distance to drainage,distance to lineament,lithology,distance to road,normalized difference vegetation index(NDVI)and land use land cover(LULC)have been considered for delineating the landslide susceptibility zonation(LSZ)map.The effect of anthropogenic activities on landslide occurrences has been examined through the distribution of landslides along national highways and land use land cover changes(LULCC).Two sets of LSZ maps with a LULC of time interval covering five years(2017&2021)were prepared to compare the temporal progression of LULC and landslide susceptibility during the five years.The results indicated the significant impact of anthropogenic activities on the landslide susceptibility.LSZ map of the year 2021 shows that 23%area falls into high and very high susceptible classes and 48%area falls into very low and low susceptibility classes.Compared to LSZ map of 2017,high and very high susceptible classes have been increased by 15%,whereas very low and low susceptible classes have been reduced by 7%.The present case study will help to understand the potential driving parameters responsible for HIL and also suggest the inclusion of LULC in landslide susceptibility analysis.The study will demonstrate new opportunities for research that could help decision-makers prepare for future disasters,both in the Indian Himalayan region and other areas.展开更多
At 12:52 pm on September 5,2022,an Ms 6.8 earthquake occurred in Luding County,Sichuan Province,China.Based on high-resolution aerial photographs and satellite imageries obtained after the earthquake,as well as field ...At 12:52 pm on September 5,2022,an Ms 6.8 earthquake occurred in Luding County,Sichuan Province,China.Based on high-resolution aerial photographs and satellite imageries obtained after the earthquake,as well as field investigation,a total of 8685 earthquake-triggered landslides(EQTLs)were interpreted.The landslides covered an area of 30.7km^(2),with a source area of 9.4 km^(2).These EQTLs were mainly distributed in areas with a seismic intensity of VIII and IX.Most of the landslides were small and medium in size,and their types included landslide,rockfall,and rock slump.Characteristic landslide distributions were found,EQTLs were distributed along the Xianshuihe fault,landslide area decreased gradually with an increased distance to the fault;EQTLs were distributed along the Daduhe River and roads;besides,landslide distribution was associated with ground deformation caused by the earthquake.EQTLs’characteristics indicated that,a large number of EQTLs were located near the foot of the slope;the full area of EQTLs and their source area followed a power function.This study concluded that Luding EQTLs were greater in number and area but relatively smaller in terms of affected area.Investigations on geo-hazards post-earthquake and risk assessment were proposed in the earthquake-stricken area to support the rehabilitation and reconstruction.展开更多
This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.Th...This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.展开更多
Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landsl...Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.展开更多
文摘A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.
基金support from the National Natural Sciences Foundation of China(Nos.42177159,42077277,41877253)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.CUG2106304).
文摘Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard require the accurate prediction of near-field wave characteristics,such as wave amplitude and run-up.However,near-field LGW involves complicated fluid-solid interactions.Furthermore,the wave characteristics are closely related to various aspects,including the geometry and physical features of the slide,river,and body of water.However,the empirical or analytical methods used for rough estimation cannot derive accurate results,especially for deformable landslides,due to their significant geometry changes during the sliding process.In this study,the near-field waves generated by deformable landslides were simulated by smoothed particle hydrodynamics(SPH)based on multi-phase flow.The deformable landslides were generalized as a kind of viscous flow by adopting the Herschel-Bulkley-Papanastasiou(HBP)-based nonNewtonian rheology model.The HBP model is capable of producing deformable landslide dynamics even though the high-speed sliding process is involved.In this study,an idealized experiment case originating from Lituya LGW and a practical case of Gongjiafang LGW were reproduced for verification and demonstration.The simulation results of both cases show satisfactory consistency with the experiment/investigation data in terms of landslide movement and near-field impulsive wave characteristics,thus indicating the applicability and accuracy of the proposed method.Finally,the effects of the HBP model’s rheological parameters on the landslide dynamics and near-field wave characteristics are discussed,providing a parameter calibration method along with sug-gestions for further applications.
文摘The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.
基金Sichuan Science and Technology Program under Grant No.2023NSFSC0894Major Project of the Science and Technology Research and Development Program of the Ministry of Railways of China under Grant No.Z2012-061。
文摘A shaking table test for a bridge foundation reinforced by anti-slide piles on a silty clay landslide model with an inclined interlayer was performed.The deformation characteristics of the bridge foundation piles and anti-slide piles were analyzed in different loading conditions.The dynamic response law of a silty clay landslide with an inclined interlayer was summarized.The spacing between the rear anti-slide piles and bridge foundation should be reasonably controlled according to the seismic fortification requirements,to avoid the two peaks in the forced deformation of the bridge foundation piles.The“blocking effect”of the bridge foundation piles reduced the deformation of the forward anti-slide piles.The stress-strain response of silty clay was intensified as the vibration wave field appeared on the slope.Since the vibration intensified,the thrust distribution of the landslide underwent a process of shifting from triangle to inverted trapezoid,the difference in the acceleration response between the bearing platform and silty clay landslide tended to decrease,and the spectrum amplitude near the natural vibration frequency increased.The rear anti-slide piles were able to slow down the shear deformation of the soil in front of the piles and avoid excessive acceleration response of the bridge foundation piles.
基金The authors gratefully acknowledge the financial support pro-vided by the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.41907232)the National Science Fund for Distinguished Young Scholars of China(Grant No.42225702)the State Key Program of National Natural Science Foundation of China(Grant No.41230636).
文摘Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.
基金funded by the National Natural Science Foundation of China(Grant No.41861134008)Muhammad Asif Khan academician workstation of Yunnan Province(Grant No.202105AF150076)+6 种基金General program of Yunnan Province Science and Technology Department(Grant No.202105AF150076)Key Project of Natural Science Foundation of Yunnan Province(Grant No.202101AS070019)Key R&D Program of Yunnan Province(Grant No.202003AC100002)General Program of basic research plan of Yunnan Province(Grant No.202001AT070059)Major scientific and technological projects of Yunnan Province:Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan(No:202202AD080010)“Study on High-Level Hidden Landslide Identification Based on Multi-Source Data”of Key Laboratory of Early Rapid Identification,Prevention and Control of Geological Diseases in Traffic Corridor of High Intensity Earthquake Mountainous Area of Yunnan Province(KLGDTC-2021-02)Guizhou Scientific and Technology Fund(QKHJ-ZK[2023]YB 193).
文摘Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金We acknowledge the funding support from the National Science Fund for Distinguished Young Scholars of National Natural Science Foundation of China(Grant No.42225702)the National Natural Science Foundation of China(Grant No.42077235).
文摘Thermo-poro-mechanical responses along sliding zone/surface have been extensively studied.However,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive failure.Here,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(i.e.wet and dry),particularly within sliding masses.Based on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and strain.The Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical monitoring.These UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure sensor.We reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the borehole.Results show that wet years are more likely to motivate landslide motions than dry years.The annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer years.The dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological cycles.These interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global destabilization.This paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.
基金supported by the National Natural Science Foundation of China project (No. 42372339)the China Geological Survey Project (Nos. DD20221816, DD20190319)。
文摘On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.
基金This research was supported by the National Natural Science Foundation of China(Grant Nos.41972284 and 42090054)This work was also supported by the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Grant No.SKLGP2020Z005).
文摘Geomorphological features are commonly used to identify potential landslides.Nevertheless,overemphasis on these features could lead to misjudgment.This research proposes a process-oriented approach for potential landslide identification that considers time-dependent behaviors.The method integrates comprehensive remote sensing and geological analysis to qualitatively assess slope stability,and employs numerical analysis to quantitatively calculate aging stability.Specifically,a time-dependent stability calculation method for anticlinal slopes is developed and implemented in discrete element software,incorporating time-dependent mechanical and strength reduction calculations.By considering the time-dependent evolution of slopes,this method highlights the importance of both geomorphological features and time-dependent behaviors in landslide identification.This method has been applied to the Jiarishan slope(JRS)on the Qinghai-Tibet Plateau as a case study.The results show that the JRS,despite having landslide geomorphology,is a stable slope,highlighting the risk of misjudgment when relying solely on geomorphological features.This work provides insights into the geomorphological characterization and evolution history of the JRS and offers valuable guidance for studying slopes with similar landslide geomorphology.Furthermore,the process-oriented method incorporating timedependent evolution provides a means to evaluate potential landslides,reducing misjudgment due to excessive reliance on geomorphological features.
基金supported by the projects of the China Geological Survey(DD20221729,DD20190291)Zhuhai Urban Geological Survey(including informatization)(MZCD–2201–008).
文摘Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.
文摘Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.
基金This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2021QZKK0202)the China Postdoctoral Science Foundation(Grant No.2021T140650)the National Natural Science Foundation of China(Grant No.42007273).The authors express their gratitude for this financial assistance.
文摘The southeastern margin of Qinghai-Tibet Plateau(SMQTP)is of a typical large landslide-prone area due to intense tectonic activity,deeply incised valleys,high geostress and frequent earthquakes.To gain insights into large landslides in southeastern margin of Qinghai-Tibet Plateau,an area covering 3.34×105 km2 that extends 80e150 km on both sides of the Sichuan-Tibet traffic corridors(G318)was used to examine the spatial distribution and corresponding characteristics of landslides.The results showed that the study area contains at least 629 large landslides that are mainly concentrated on 7 zones(zones IeVII).Zones IeVII are in the southern section of the Longmenshan fault zone(with no large river)and sections with Dadu River,Jinsha River,Lancang River,Nujiang River and Yarlung Zangbo River.There are more landslides in the Jinsha River section(totaling 186 landslides)than the other sections.According to the updated Varnes classification,408 large landslides(64.9%)were recognized and divided into 4 major types,i.e.flows(275 cases),slides(58 cases),topples(44 cases)and slope deformations(31 cases).Flows,which consist of rock avalanches and iceerock avalanches,are the most common landslide type.Large landslide triggers(178 events,28.3%)are also recognized,and earthquakes may be the most common trigger.Due to the limited data,these landslide type classifications and landslide triggers are perhaps immature,and further systematic analysis is needed.
基金funded by the Natural Science Foundation of China(Grant Nos.52079062 and 41807285)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University,China(Grant No.9167-28220007-YB2107).
文摘Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.
文摘There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.
基金supported by the National Natural Science Foundation of China(No.U21A2031)Key R&D Program of Hubei Province(No.2022BAA047)+3 种基金China Postdoctoral Science Foundation(No.2021M701969)Open Fund of Key Laboratory of Geological Hazards on Three Gorges Reservoir Area(2022KDZ19)the Open Fund of Badong National Observation and Research Station of Geohazards(No.BNORSG-202207No.BNORSG-202304)。
文摘After the impoundment of the Three Gorges Reservoir,some huge ancient landslides were reactivated and deformed,showing typical hydrodynamic pressure landslide characteristics.The Baishuihe landslide was a typical hydrodynamic pressure landslide.The management department conducted slope cutting treatments from 2018 to 2019.To evaluate the treatment effect of rear slope cutting,this study analyzed the data of the surface deformation survey and field monitoring over the past 20 years and the characteristics of the reservoir water-triggered Baishuihe landslide deformation,and calculated the seepage field,displacement field,and stability coefficient before and after landslide treatment.The results showed that the deformation of the Baishuihe landslide was primarily related to a decrease in the reservoir water level.Owing to the poor permeability of the landslide soil,the decrease in the reservoir water level produced a seepage force pointing to the outside of the landslide body,leading to the step deformation of the landslide displacement.The landslide was treated by rear slope cutting,and the“step”deformation of the landslide disappeared after treatment.The hydrodynamic pressure caused by the change in reservoir water after cutting the slope did not disappear.However,as the slope cutting greatly reduced the overall sliding force of the landslide,its stability was greatly improved.Notably,high stability can still be ensured under extreme rainfall after treatment.Slope cutting is effective for treating hydrodynamic pressure landslides.This study can provide effective technical support for the treatment of reservoir landslides.
文摘Landslides in the Himalayan region are primarily controlled by natural parameters,including rainfall,seismic activity,and anthropogenic parameters,such as the construction of large-scale projects like road development,tunneling and hydroelectric power projects and climate change.The parameters which are more crucial among these are a matter of scientific study and analysis.This research,taking Solan district,Himachal Pradesh,India,as the study area,aims to assess the impact of anthropogenic activities on landslide susceptibility at a regional scale.Landslide distribution was characterized into two groups,namely Rainfall-Induced Landslide(RIL)and Human-Induced Landslide(HIL)based on triggering factors.Multiple data such as slope angle,aspect,profile curvature,distance to drainage,distance to lineament,lithology,distance to road,normalized difference vegetation index(NDVI)and land use land cover(LULC)have been considered for delineating the landslide susceptibility zonation(LSZ)map.The effect of anthropogenic activities on landslide occurrences has been examined through the distribution of landslides along national highways and land use land cover changes(LULCC).Two sets of LSZ maps with a LULC of time interval covering five years(2017&2021)were prepared to compare the temporal progression of LULC and landslide susceptibility during the five years.The results indicated the significant impact of anthropogenic activities on the landslide susceptibility.LSZ map of the year 2021 shows that 23%area falls into high and very high susceptible classes and 48%area falls into very low and low susceptibility classes.Compared to LSZ map of 2017,high and very high susceptible classes have been increased by 15%,whereas very low and low susceptible classes have been reduced by 7%.The present case study will help to understand the potential driving parameters responsible for HIL and also suggest the inclusion of LULC in landslide susceptibility analysis.The study will demonstrate new opportunities for research that could help decision-makers prepare for future disasters,both in the Indian Himalayan region and other areas.
基金supported by the“Second Tibetan Plateau Scientific Expedition and Research Program(STEP)”(Grant No.2019QZKK0902)the Strategic Priority Research Program of the CAS(No.XDA23090203)。
文摘At 12:52 pm on September 5,2022,an Ms 6.8 earthquake occurred in Luding County,Sichuan Province,China.Based on high-resolution aerial photographs and satellite imageries obtained after the earthquake,as well as field investigation,a total of 8685 earthquake-triggered landslides(EQTLs)were interpreted.The landslides covered an area of 30.7km^(2),with a source area of 9.4 km^(2).These EQTLs were mainly distributed in areas with a seismic intensity of VIII and IX.Most of the landslides were small and medium in size,and their types included landslide,rockfall,and rock slump.Characteristic landslide distributions were found,EQTLs were distributed along the Xianshuihe fault,landslide area decreased gradually with an increased distance to the fault;EQTLs were distributed along the Daduhe River and roads;besides,landslide distribution was associated with ground deformation caused by the earthquake.EQTLs’characteristics indicated that,a large number of EQTLs were located near the foot of the slope;the full area of EQTLs and their source area followed a power function.This study concluded that Luding EQTLs were greater in number and area but relatively smaller in terms of affected area.Investigations on geo-hazards post-earthquake and risk assessment were proposed in the earthquake-stricken area to support the rehabilitation and reconstruction.
基金the National Natural Science Foundation of China(42077259).
文摘This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.
基金supported by National Natural Science Foundation of China(Grant Nos.42090054,52027814 and 41772376)the Open Fund of the Technology Innovation Center for Automated Geological Disaster Monitoring,Ministry of Natural Resources(Grant No.2022058014)。
文摘Compared with the study of single point motion of landslides,studying landslide block movement based on data from multiple monitoring points is of great significance for improving the accurate identification of landslide deformation.Based on the study of landslide block,this paper regarded the landslide block as a rigid body in particle swarm optimization algorithm.The monitoring data were organized to achieve the optimal state of landslide block,and the 6-degree of freedom pose of the landslide block was calculated after the regularization.Based on the characteristics of data from multiple monitoring points of landslide blocks,a prediction equation for the motion state of landslide blocks was established.By using Kalman filtering data assimilation method,the parameters of prediction equation for landslide block motion state were adjusted to achieve the optimal prediction.This paper took the Baishuihe landslide in the Three Gorges reservoir area as the research object.Based on the block segmentation of the landslide,the monitoring data of the Baishuihe landslide block were organized,6-degree of freedom pose of block B was calculated,and the Kalman filtering data assimilation method was used to predict the landslide block movement.The research results showed that the proposed prediction method of the landslide movement state has good prediction accuracy and meets the expected goal.This paper provides a new research method and thinking angle to study the motion state of landslide block.
