无限边坡稳定性(stability index mapping,SINMAP)模型在评价降雨作用下各孕灾因子对滑坡的贡献率时较为随机,分形理论方法可弥补SINMAP模型的单尺度分析局限,通过优化输入参数来研究各孕灾因子对滑坡发育的贡献率.本文以山西省临县为例...无限边坡稳定性(stability index mapping,SINMAP)模型在评价降雨作用下各孕灾因子对滑坡的贡献率时较为随机,分形理论方法可弥补SINMAP模型的单尺度分析局限,通过优化输入参数来研究各孕灾因子对滑坡发育的贡献率.本文以山西省临县为例,首先使用分形理论探究各滑坡孕灾因子对临县滑坡发育规律的影响.随后基于SINMAP模型对不同降雨强度等级下研究区内地表斜坡稳定性进行评价.结果表明:坡度、坡向2种因子和滑坡灾害之间存在二阶累计变维分形,而高程因子与滑坡灾害间具有三阶累计变维分形关系,与滑坡发育相关性最高;随着降雨强度的增大,研究区内不稳定区域面积占比由35.51%增长至49.71%,稳定区域逐渐向失稳区域转变,呈现出由中心向四周逐渐变差的分布状态,且滑坡密度始终保持在极不稳定区最大,极稳定区最小.本研究可为临县的黄土滑坡灾害减灾防灾提供参考.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
文摘无限边坡稳定性(stability index mapping,SINMAP)模型在评价降雨作用下各孕灾因子对滑坡的贡献率时较为随机,分形理论方法可弥补SINMAP模型的单尺度分析局限,通过优化输入参数来研究各孕灾因子对滑坡发育的贡献率.本文以山西省临县为例,首先使用分形理论探究各滑坡孕灾因子对临县滑坡发育规律的影响.随后基于SINMAP模型对不同降雨强度等级下研究区内地表斜坡稳定性进行评价.结果表明:坡度、坡向2种因子和滑坡灾害之间存在二阶累计变维分形,而高程因子与滑坡灾害间具有三阶累计变维分形关系,与滑坡发育相关性最高;随着降雨强度的增大,研究区内不稳定区域面积占比由35.51%增长至49.71%,稳定区域逐渐向失稳区域转变,呈现出由中心向四周逐渐变差的分布状态,且滑坡密度始终保持在极不稳定区最大,极稳定区最小.本研究可为临县的黄土滑坡灾害减灾防灾提供参考.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
