Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding...Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant No.42407221)the Open Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Grant No.SKLGP2024K009)the Hubei Provincial Natural Science Foun-dation,China(Grant No.2023AFB567).
文摘Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.
