灾害学2024，Vol.39Issue(2)：21-25,72,6.DOI:10.3969/j.issn.1000-811X.2024.02.004
基于随机森林算法的云南昆磨高速公路气象风险研究
Meteorological Risk Research of Kunmo Expressway in Yunnan Province Based on Random Forest Algorithm
摘要
Abstract
Based on the data of 22 traffic meteorological observation stations along Kunming-Mohan Express-way from 2018 to 2021,the total precipitation and the number of days with visibility less than 500 meters are cal-culated.The kernel density of ground disaster points,tunnel points and bridge points and the grid data of road cur-vature radius are input into the prediction model based on random forest algorithm.Finally,the R2 value of the re-gression prediction result of the dangerous section of Kunming-Mohan Expressway is 0.790,and the P value is 0.001,which meets the requirements of significance test,and the prediction result is highly fitted with the verifi-cation data.The results show that:①The sections above moderate risk are concentrated in Chenggong,Jinning,Hongta,Eshan,Ning'er and Jinghong City along the upper-middle section of Kunmo Expressway,among which the sections with major risk levels are mainly distributed in Jinghong City and Ning'er County;②According to the prediction results of random forest algorithm,the importance of tunnel point nuclear density accounts for 26％,the importance of days with visibility less than 500 meters accounts for 24％,and the two comprehensively ac-counts for 50％,indicating that the distribution of tunnels and the weather conditions with low visibility have the greatest impact on the driving safety along Kunmo Expressway.关键词
公路气象风险/随机森林预测/机器学习/昆磨高速Key words
highway meteorological risk/random forest prediction/machine learning/Kunmo Expressway分类
资源环境引用本文复制引用
向曦,王鑫瑞,彭启洋,彭艳秋..基于随机森林算法的云南昆磨高速公路气象风险研究[J].灾害学,2024,39(2):21-25,72,6.基金项目
云南省社会发展专项(202203AC100006) （202203AC100006）
云南省政府决策咨询课题(ZFKKT-2021-096) （ZFKKT-2021-096）
云南大学第十四届研究生科研创新项目(KC-22222292) （KC-22222292）
