| 注册
首页|期刊导航|中国地质灾害与防治学报|低山丘陵区公路地质灾害气象预报模型对比及应用

低山丘陵区公路地质灾害气象预报模型对比及应用

周雨 肖雯 李三角 谢克勇

中国地质灾害与防治学报2023,Vol.34Issue(6):77-85,9.
中国地质灾害与防治学报2023,Vol.34Issue(6):77-85,9.DOI:10.16031/j.cnki.issn.1003-8035.202303039

低山丘陵区公路地质灾害气象预报模型对比及应用

Comparison and application on meteorological forecast models of geological hazards for highways in low mountain and hilly area:A case study along the highways in Jiangxi Province

周雨 1肖雯 1李三角 1谢克勇1

作者信息

  • 1. 江西省气象服务中心,江西南昌 330096
  • 折叠

摘要

Abstract

In order to improve the prediction and early warning of road geological hazards and mitigate the impact of heavy rainfall on the safety of high-speed driving in mountainous areas,this paper combines precipitation data from national meteorological stations with data from traffic meteorological stations along highways in Jiangxi Province.Based on the analysis of the geological environment conditions and rainfall characteristics along the highways,four machine learning methods including Support Vector Machine(SVM),logical regression,K neighbors and random forest were adopted to do research on the highway geological disaster forecast modeling and early warning test.The results show that:(1)The majority of geological disasters along Jiangxi highways are located at altitudes of 300 to 450 meters,with slope gradients mostly ranging from 20° to 35°.As terrain slope increases,a unimodal distribution of hazards is observed.Regions with dense river networks and certain vegetation coverage are more prone to experiencing geological hazards.(2)Three main types of rainfall inducing highway geological hazards are identified:long-term rainfall,short-term rainfall,and short-time rainfall.(3)Comparative assessment of four kinds of geological hazard machine learning methods dedicated to geological disasters demonstrates that,for rainfall-induced geological hazards,all four predictive models achieve accuracies exceeding 0.75.Further study found that the logistic regression and random forest model outperform others in forecasting accuracy for both long and short rainfall periods,while the K-neighbor approach was better for short-term rainfall forecast.

关键词

山区公路/地质灾害/机器学习/气象预报

Key words

mountain highways/geological disasters/machine learning methords/weather forecasting

分类

天文与地球科学

引用本文复制引用

周雨,肖雯,李三角,谢克勇..低山丘陵区公路地质灾害气象预报模型对比及应用[J].中国地质灾害与防治学报,2023,34(6):77-85,9.

基金项目

江西省重点研发计划项目(20202BBGL73100) (20202BBGL73100)

2021年度江西省03专项及5G项目(20212ABC03A29) (20212ABC03A29)

江西省气象局重点项目(JX2020Z11) (JX2020Z11)

中国地质灾害与防治学报

OACSCDCSTPCD

1003-8035

访问量8
|
下载量0
段落导航相关论文