水利水电技术（中英文）2025，Vol.56Issue(11)：71-82,12.DOI:10.13928/j.cnki.wrahe.2025.11.006
基于分类递归特征消除法-随机森林优化算法的山洪灾害风险模拟技术
Flash flood disaster risk simulation technology based on classified recursive feature elimination-random forest optimization algorithm
摘要
Abstract
[Objective]Flash flood disasters cause severe economic losses and casualties to human society,making the scientific identification and assessment of flash flood disaster risk an urgent issue to be addressed.The aim of the study is to improve the accuracy of flash flood risk prediction by coupling feature selection with the Random Forest algorithm,thereby providing a scientific basis for disaster early warning.[Methods]Seventeen feature factors associated with the occurrence of flash flood disasters were selected.A feature selection approach that integrated Classified Recursive Feature Elimination(RFE-class)with a Random Forest optimization algorithm was proposed to identify the optimal feature combination for flash flood risk simulation.[Results]The result showed that the optimal feature combination obtained using the RFE-class method significantly improved the predictive performance of the Random Forest model,achieving a Receiver Operating Characteristic(ROC)curve value of 94.7％,representing an approximately 5％improvement in accuracy compared to using the Random Forest algorithm alone.[Conclusion]In Fujian Province,the high-risk areas for flash flood disasters are primarily distributed in the Wuyi Mountains,Daiyun Mountains,and Daimao Mountain regions,covering an area of approximately 49 000 km2 and affecting 27 million people.关键词
山洪灾害风险/随机森林/递归特征选择法/小流域尺度/影响因素Key words
flash flood disaster risk/Random Forest/recursive feature elimination method/small watershed scale/influencing factors分类
建筑与水利引用本文复制引用
ZHANG Xiaolei,QIN Ruihua,YAO Qiuling,DONG Changqi,LIU Ronghua..基于分类递归特征消除法-随机森林优化算法的山洪灾害风险模拟技术[J].水利水电技术（中英文）,2025,56(11):71-82,12.基金项目
国家自然科学基金项目(42201093,52239006) （42201093,52239006）
