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基于机器学习的滑坡崩塌地质灾害气象风险预警研究

李阳春 刘黔云 李潇 顾天红 张楠

李阳春, 刘黔云, 李潇, 顾天红, 张楠. 2021: 基于机器学习的滑坡崩塌地质灾害气象风险预警研究. 中国地质灾害与防治学报, 32(3): 118-123. doi: 10.16031/j.cnki.issn.1003-8035.2021.00-15
引用本文: 李阳春, 刘黔云, 李潇, 顾天红, 张楠. 2021: 基于机器学习的滑坡崩塌地质灾害气象风险预警研究. 中国地质灾害与防治学报, 32(3): 118-123. doi: 10.16031/j.cnki.issn.1003-8035.2021.00-15
Yangchun LI, Qianyun LIU, Xiao LI, Tianhong GU, Nan ZHANG. 2021: Exploring early warning and forecasting of meteorological risk of landslide and rockfall induced by meteorological factors by the approach of machine learning. The Chinese Journal of Geological Hazard and Control, 32(3): 118-123. doi: 10.16031/j.cnki.issn.1003-8035.2021.00-15
Citation: Yangchun LI, Qianyun LIU, Xiao LI, Tianhong GU, Nan ZHANG. 2021: Exploring early warning and forecasting of meteorological risk of landslide and rockfall induced by meteorological factors by the approach of machine learning. The Chinese Journal of Geological Hazard and Control, 32(3): 118-123. doi: 10.16031/j.cnki.issn.1003-8035.2021.00-15

基于机器学习的滑坡崩塌地质灾害气象风险预警研究

doi: 10.16031/j.cnki.issn.1003-8035.2021.00-15
详细信息
    作者简介:

    李阳春(1983-),男,湖北武汉人,硕士,高级工程师,研究方向为地质灾害综合防治。E-mail:82066240@qq.com

    通讯作者:

    张 楠(1987-),男,重庆綦江人,本科,高级工程师,研究方向为地质灾害气象风险预警。E-mail:675400947@qq.com

  • 中图分类号: TP694

Exploring early warning and forecasting of meteorological risk of landslide and rockfall induced by meteorological factors by the approach of machine learning

  • 摘要: 在划分气象风险等级时,传统地质灾害气象风险预警方法忽略了承灾体脆弱性因素,且气象风险预报等级整体偏高,导致高等级风险区空报率较高。基于此,提出基于机器学习的滑坡、崩塌灾害气象风险预警方法。利用信息量法,分析气象因素影响程度。选取坐标点、降雨量、易发生等级,将其作为机器学习人工神经网络的输入节点,判断是否发生崩塌、滑坡灾害;针对地质灾害区域,根据影响程度计算气象引发因子指数,结合滑坡、崩塌灾害潜势度G和承灾体脆弱性M,确定气象风险预警指数R,划分预警级别,完成滑坡、崩塌灾害气象风险预警。实验结果表明,设计方法有效降低了三级预报和四级预警空报率,提升了预警精细化程度。
  • 图  1  贵州省地质灾害易发区分布示意图

    Figure  1.  Distribution of geological disaster-prone areas in Guizhou Province

    图  2  滑坡、崩塌灾害机器学习神经网络结构

    Figure  2.  Structure of machine learning neural network for geological disasters

    图  3  贵州省降水量变化

    Figure  3.  Precipitation change in Guizhou Province

    图  4  崩塌预警结果

    Figure  4.  Collapse forecast and early warning results

    图  5  滑坡预警结果

    Figure  5.  Landslide forecast and early warning results

    表  1  滑坡、崩塌灾害高易发区气象风险预警级别

    Table  1.   Early warning level of meteorological risk in high areas prone to geological disasters

    累积降水
    /mm
    预报小雨
    0.01~10
    预报中雨
    10~25
    预报大雨
    25~50
    预报暴雨
    50~100
    预报大暴雨
    ≥100
    ≤30 蓝色黄色橙色红色
    30~50蓝色黄色橙色红色红色
    50~100黄色橙色红色红色红色
    ≥100橙色红色红色红色红色
    下载: 导出CSV

    表  2  滑坡、崩塌灾害中易发区气象风险预警级别

    Table  2.   Warning level of meteorological risk in areas prone to geological disasters

    累积降水
    /mm
    预报小雨
    0.01~10
    预报中雨
    10~25
    预报大雨
    25~50
    预报暴雨
    50~100
    预报大暴雨
    ≥100
    ≤30 蓝色黄色橙色
    30~50蓝色黄色橙色红色
    50~100蓝色黄色橙色红色红色
    ≥100黄色橙色红色红色红色
    下载: 导出CSV

    表  3  滑坡、崩塌灾害低易发区气象风险预警级别

    Table  3.   Early warning level of meteorological risk in low areas prone to geological disasters

    累积降水
    /mm
    预报小雨
    0.01~10
    预报中雨
    10~25
    预报大雨
    25~50
    预报暴雨
    50~100
    预报大暴雨
    ≥100
    ≤30 蓝色黄色
    30~50蓝色黄色橙色
    50~100蓝色黄色橙色红色
    ≥100蓝色黄色橙色红色红色
      注:其中预报降水为24 h预报降雨量,累积降水为最近五天累计降雨量。
    下载: 导出CSV

    表  4  贵州省当日临界雨量和5日临界雨量

    Table  4.   Critical rainfall and mm rainfall of 5 th Day of Guizhou Province

    灾害易发区域一级二级三级四级
    当日临界雨量
    /m
    不易发区92553728
    低易发区110674534
    中易发区132795340
    高易发区25415110176
    5日临界雨量
    /m
    不易发区2231338967
    低易发区24315710377
    中易发区26215710579
    高易发区30418112191
    下载: 导出CSV

    表  5  贵州省典型地质灾害统计数据

    Table  5.   Statistical data of typical geological disasters in Guizhou Province

    灾害点类型灾害点数量/个分布市镇数量/个占灾害点总数比例/%
    滑坡10325885.7%
    崩塌111199.2%
    泥石流29122.4%
    地面塌陷2582.1%
    地裂缝730.5%
    下载: 导出CSV

    表  6  崩塌预警空报率

    Table  6.   Empty reporting rate of collapse early warning and forecast

    设计方法常规方法1常规方法2
    一级预报/%000
    二级预报/%000
    三级预报/%8.2714.9217.92
    四级预警/%7.2613.2919.26
    下载: 导出CSV

    表  7  滑坡预警空报率

    Table  7.   Empty reporting rate of landslide early warning and forecast

    设计方法常规方法1常规方法2
    一级预报/%000
    二级预报/%001.21
    三级预报/%9.9214.9616.92
    四级预警/%6.1214.6317.29
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-29
  • 修回日期:  2021-05-25
  • 刊出日期:  2021-06-25

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