河南城建学院学报2024，Vol.33Issue(4)：91-99,127,10.DOI:10.14140/j.cnki.hncjxb.2024.04.012
基于多模型耦合的永嘉县滑坡易发性评价
Landslide susceptibility assessment of Yongjia County based on multi-model coupling
摘要
Abstract
Regional landslide susceptibility assessment is an important basis for formulating landslide disaster prevention and mitigation schemes and carrying out land and resources planning and utilization.Taking Yongjia County,Zhejiang Province as an example,on the basis of analyzing landslide disaster conditions,13 evaluation factors including elevation,slope,curvature,topographic position index(TPI),topographic undulation,slope aspect,stratum lithology,distance from fault,distance from water system,average annual rainfall,average annu-al wind speed,distance from road and normalized difference vegetation index(NDVI)were selected,and logis-tic regression-information content(LR-I)coupled model and logistic regression-weight of evidence(LR-WOE)coupled model were used to evaluate landslide susceptibility.The results show that seven factors,such as aspect,distance from fault,distance from water system,curvature,slope,distance from road and terrain posi-tion index(TPI),have great influence on landslide susceptibility assessment.Among the high-prone areas and extremely high-prone areas obtained by LR-I model,the number of landslides account for 86.67%and the landslide area account for 36.96%.Among the high-prone areas and extremely high-prone areas obtained by LR-WOE model,the number of landslides account for 86.19%and the landslide area account for 36.13%.The accuracy of both coupled models is above 0.8,showing high accuracy and good prediction performance.The research results have certain reference value for landslide disaster prevention and mitigation in this area.关键词
滑坡/易发性评价/永嘉县/评价因子/多模型耦合Key words
landslide/susceptibility assessment/Yongjia County/assessment factor/multi-model coupling分类
天文与地球科学引用本文复制引用
杨冰颖,缪海波,马闯,崔玉龙..基于多模型耦合的永嘉县滑坡易发性评价[J].河南城建学院学报,2024,33(4):91-99,127,10.基金项目
国家自然科学基金(42277136) （42277136）
安徽省自然科学基金(2208085MD97) （2208085MD97）
安徽高校自然科学研究项目(2022AH050806) （2022AH050806）
