Landslides are prevalent,regular,and expensive hazards in the Karakoram Highway(KKH)region.The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor(CPEC)context.This region has not only imm...Landslides are prevalent,regular,and expensive hazards in the Karakoram Highway(KKH)region.The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor(CPEC)context.This region has not only immense economic importance but also ecological significance.The purpose of the study was to map the landslide-prone areas along KKH using two different techniquesAnalytical Hierarchy Process(AHP)and Scoops 3 D model.The causative parameters for running AHP include the lithology,presence of thrust,land use land cover,precipitation,and Digital Elevation Model(DEM)derived variables(slope,curvature,aspect,and elevation).The AHP derived final landslide susceptibility map was classified into four zones,i.e.,low,moderate,high,and extremely high.Over 80%of the study area falls under the moderate(43%)and high(40%)landslide susceptible zones.To assess the slope stability of the study area,the Scoops 3 D model was used by integrating with the earthquake loading data.The results of the limit equilibrium analysis categorized the area into four groups(low,moderate,high,and extremely high mass)of slope failure.The areas around Main Mantle Thrust(MMT)including Dubair,Jijal,and Kohistan regions,had high volumes of potential slope failures.The results from AHP and Scoops 3 D techniques were validated with the landslides inventory record of the Geological Survey of Pakistan and Google Earth.The results from both the techniques showed similar output that coincides with the known landslides areas.However,Scoops 3 D provides not only susceptible zones but also the range of volume of the potential slope failures.Further,these techniques could be used in other mountainous areas,which could help in the landslide mitigation measures.展开更多
Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitu...Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.展开更多
文摘Landslides are prevalent,regular,and expensive hazards in the Karakoram Highway(KKH)region.The KKH connects Pakistan with China in the present China-Pakistan Economic Corridor(CPEC)context.This region has not only immense economic importance but also ecological significance.The purpose of the study was to map the landslide-prone areas along KKH using two different techniquesAnalytical Hierarchy Process(AHP)and Scoops 3 D model.The causative parameters for running AHP include the lithology,presence of thrust,land use land cover,precipitation,and Digital Elevation Model(DEM)derived variables(slope,curvature,aspect,and elevation).The AHP derived final landslide susceptibility map was classified into four zones,i.e.,low,moderate,high,and extremely high.Over 80%of the study area falls under the moderate(43%)and high(40%)landslide susceptible zones.To assess the slope stability of the study area,the Scoops 3 D model was used by integrating with the earthquake loading data.The results of the limit equilibrium analysis categorized the area into four groups(low,moderate,high,and extremely high mass)of slope failure.The areas around Main Mantle Thrust(MMT)including Dubair,Jijal,and Kohistan regions,had high volumes of potential slope failures.The results from AHP and Scoops 3 D techniques were validated with the landslides inventory record of the Geological Survey of Pakistan and Google Earth.The results from both the techniques showed similar output that coincides with the known landslides areas.However,Scoops 3 D provides not only susceptible zones but also the range of volume of the potential slope failures.Further,these techniques could be used in other mountainous areas,which could help in the landslide mitigation measures.
基金funded by the Sichuan Transportation Science and Technology Project(Grant No.2018-ZL-01)High-end Foreign Expert Introduction program(Grant No.G2022165004L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001).
文摘Landslide susceptibility mapping is an integral part of geological hazard analysis.Recently,the emphasis of many studies has been on data-driven models,notably those derived from machine learning,owing to their aptitude for tackling complex non-linear problems.However,the prevailing models often disregard qualitative research,leading to limited interpretability and mistakes in extracting negative samples,i.e.inaccurate non-landslide samples.In this study,Scoops 3D(a three-dimensional slope stability analysis tool)was utilized to conduct a qualitative assessment of slope stability in the Yunyang section of the Three Gorges Reservoir area.The depth of the bedrock was predicted utilizing a Convolutional Neural Network(CNN),incorporating local boreholes and building on the insights from prior research.The Random Forest(RF)algorithm was subsequently used to execute a data-driven landslide susceptibility analysis.The proposed methodology demonstrated a notable increase of 29.25%in the evaluation metric,the area under the receiver operating characteristic curve(ROC-AUC),outperforming the prevailing benchmark model.Furthermore,the landslide susceptibility map generated by the proposed model demonstrated superior interpretability.This result not only validates the effectiveness of amalgamating mathematical and mechanistic insights for such analyses,but it also carries substantial academic and practical implications.
