地质与资源2025，Vol.34Issue(1)：77-86,10.DOI:10.13686/j.cnki.dzyzy.2025.01.009
基于信息量法和集成学习算法的地质灾害易发性评价
Assessment of geological hazard susceptibility based on information method and ensemble learning algorithm:A case study of Harbin City in Heilongjiang Province
摘要
Abstract
To carry out the geological hazard susceptibility zoning and prevention in Harbin,Heilongjiang Province,eight evaluation factors including slope gradient,slope aspect,curvature,lithology,NDVI,distance from river,distance from road and distance from structure are selected to establish the evaluation index system of geological hazard susceptibility.The non-geological hazard samples are randomly chosen from the extremely low and low susceptible zones calculated by information algorithm,which forms the document data set together with the geological hazard samples.Besides,three ensemble learning methods such as random forest(RF),Adaboost and Stacking are used to assess the geological hazard vulnerability in Harbin,and the accuracy is verified by confusion matrix.The results show that the trend of evaluation results of the four algorithms is the same,and consistent with the actual situation of the study area.The major inducing factor of geological hazards in Harbin is human engineering activities,with the extremely high susceptible zones mainly concentrated near roads.The area of extremely high susceptible zones predicted by RF algorithm accounts for only 1.27%of the whole region,yet the number of geological hazards takes up 21.03%,with the frequency ratio of 16.58 and the maximum AUC value reaching 0.891,indicating that RF algorithm has more advantages in the geological hazard susceptibility evaluation among the above three algorithms.关键词
地质灾害/易发性评价/信息量法/集成学习算法/哈尔滨市Key words
geological hazard/susceptibility assessment/information method/ensemble learning algorithm/Harbin City分类
天文与地球科学引用本文复制引用
李蕴峰,卢彦达,陈卓,卢昱润,李涛涛..基于信息量法和集成学习算法的地质灾害易发性评价[J].地质与资源,2025,34(1):77-86,10.基金项目
中国地质调查局项目"应用地质调查数据应用服务"(DD20230595、DD20230594). （DD20230595、DD20230594）
