西南交通大学学报2025，Vol.60Issue(2)：282-289,8.DOI:10.3969/j.issn.0258-2724.20220864
基于聚类和随机森林耦合模型的崩滑灾害易发性评估
Susceptibility Assessment of Collapses and Landslides Based on Cluster and Random Forest Coupled Model
摘要
Abstract
Hazard susceptibility assessment is generally a probabilistic modeling based on the spatial distribution characteristics of hazards,but the hazards have spatial heterogeneity.In order to solve the spatial heterogeneity of hazards,the collapses and landslides along the Wenchuan-Lixian section in the Wenchuan-Maerkang Expressway were studied.By using the K-means algorithm,the hazard-threaten objects(people and property)and risk degree(damaged house area and damaged road length)in the study area were spatially clustered,and different clustering attributes were assigned to the study area.Then,nine factors including slope angle,elevation,slope aspect,curvature,surface cutting degree,river curvature coefficient,distance from the tectonic zone,bank slope structure,and formation lithology were selected in terms of hydrology,geology,and geomorphic conditions.The samples were divided into 70%training data and 30%test data.The performance of the K-RF model and the traditional random forest(RF)model in susceptibility assessment was compared to provide theoretical support for operation safety and hazard prevention of expressways.The results show that the K-RF model contains a total of 82.95%of the hazard points in the areas with extremely high susceptibility,which has better assessment results than the single RF model(with AUC value increased by 5.4%).Therefore,it is feasible to use clustering to solve the spatial heterogeneity of hazards.However,the research limitation is that it fails to reflect the spatial heterogeneity of hazards from the hazard itself.The coupled model is a comprehensive reflection of susceptibility and vulnerability in essence.关键词
崩滑灾害/机器学习/易发性/空间异质性Key words
hazard/machine learning/susceptibility/spatial heterogeneity分类
资源环境引用本文复制引用
杜映锦,席传杰,胡卸文,吴建利,刘波,何坤..基于聚类和随机森林耦合模型的崩滑灾害易发性评估[J].西南交通大学学报,2025,60(2):282-289,8.基金项目
国家重点研发计划(2018YFC1505401) （2018YFC1505401）
