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基于AHP-突变理论组合模型的地质灾害危险性评价以河北平山县为例

于开宁, 吴涛, 魏爱华, 武玉璞, 代锋刚, 刘煜

于开宁,吴涛,魏爱华,等. 基于AHP-突变理论组合模型的地质灾害危险性评价−以河北平山县为例[J]. 中国地质灾害与防治学报,2023,34(2): 146-155. DOI: 10.16031/j.cnki.issn.1003-8035.202201012
引用本文: 于开宁,吴涛,魏爱华,等. 基于AHP-突变理论组合模型的地质灾害危险性评价−以河北平山县为例[J]. 中国地质灾害与防治学报,2023,34(2): 146-155. DOI: 10.16031/j.cnki.issn.1003-8035.202201012
YU Kaining,WU Tao,WEI Aihua,et al. Geological hazard assessment based on the models of AHP, catastrophe theory and their combination: A case study in Pingshan County of Hebei Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(2): 146-155. DOI: 10.16031/j.cnki.issn.1003-8035.202201012
Citation: YU Kaining,WU Tao,WEI Aihua,et al. Geological hazard assessment based on the models of AHP, catastrophe theory and their combination: A case study in Pingshan County of Hebei Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(2): 146-155. DOI: 10.16031/j.cnki.issn.1003-8035.202201012

基于AHP-突变理论组合模型的地质灾害危险性评价——以河北平山县为例

基金项目: 河北省重点地质灾害勘查项目(2019150);河北省自然科学基金(D2022403032)
详细信息
    作者简介:

    于开宁(1965-),男,山东乳山人,博士,教授,主要从事水资源与环境地质研究工作。E-mail:1211931193@qq.com

    通讯作者:

    刘 煜(1982-),男,河北石家庄人,学士,高级工程师,主要从事地质灾害防治、地质环境监测与演化研究工作。E-mail:16222420@qq.com

  • 中图分类号: P694

Geological hazard assessment based on the models of AHP, catastrophe theory and their combination: A case study in Pingshan County of Hebei Province

  • 摘要:

    河北平山县受地形地貌、地质构造和生态环境等因素的影响,崩滑流等地质灾害频发。选取地形起伏度、坡度、坡向、河网密度、断裂带密度、地层岩性、NDVI、土地利用类型及地质灾害点密度9个评价因子,用AHP和突变理论分别求各评价因子权重,并按最小信息熵权法结合,建立AHP-突变理论组合模型并应用,对比基于三种方法的平山县地质灾害危险性评价结果。结果表明:组合模型的评价结果精度更高,符合该区地质灾害发育特征;组合模型法将主客观结合,综合考虑因子的影响,评价结果可靠。该研究为平山县及类似地区地质灾害危险性评价提供一种新的尝试和方法。

    Abstract:

    Pingshan County, Hebei was affected by topography, geological structure, ecological environment and other factors, geological disasters such as landslides occurred frequently. Nine evaluation factors including topographic relief, slope, aspect, river network density, fault zone density, stratigraphic lithology, NDVI, land use type and geological disaster point density were selected. The weights of each evaluation factor were calculated by AHP and catastrophe theory, and the combination model of AHP and catastrophe theory was established and applied according to the minimum information entropy weight method. The results of geological disaster risk assessment in Pingshan County based on three methods were compared. The results show that the evaluation results of the combined model have higher accuracy and are in line with the development characteristics of geological disasters in this area. Combined model method combines subjective and objective, considering the influence of factors, the evaluation results are reliable. This study provides a new attempt and method for geological disaster risk assessment in Pingshan County and similar areas.

  • 滑坡、崩塌灾害是地质灾害中的重要灾种,对社会经济、生命财产、地理环境都产生了重要影响[1]。贵州是地质灾害高发易发地区,常见诱发因素主要为降雨[2]。因此,研究地质灾害气象风险预警方法,对地质灾害防治具有重要作用。研究者对该领域进行了很多研究。文献[3]应用GIS数据处理功能,分析地质灾害区域的工程地质,针对复杂地质的地理环境,绘制出各气象风险等级下区域地质灾害分布图,但该方法对气象因素提取不全面,造成气象风险预警等级整体偏高。文献[4]分析气象环境与降水量之间的规律性,包括降雨阈值和临界降雨量等,通过地质灾害调查统计,综合评价气象风险,但该方法对降雨量的相关性分析较差,划分的气象风险预警等级同样较高。

    针对以上问题,提出基于机器学习的滑坡、崩塌灾害气象风险预警方法。借助机器学习算法中人工神经网络实现贵州省滑坡、崩塌灾害气象风险预警方法的研究。实验结果表明:采用设计方法有效降低了滑坡、崩塌三级、四级预警空报率,提升了预警精细化程度。

    为实现贵州省滑坡、崩塌灾害气象风险预警,需要提取贵州省滑坡、崩塌灾害相关气象因素,计算气象因素对滑坡、崩塌灾害的影响程度。汇总贵州省各区域的地质灾害详细调查报告、气象局实测降雨量数据,采用资料查阅、实地访问调查等方式,以月报形式统计气象引发灾害事件,同时综合考虑GPS、天气雷达、闪电定位、自动雨量站等数据来源,采集非常规观测资料,获取灾害隐患点数据和灾害易发分区数据。筛选与滑坡、崩塌灾害相关的气象因素。将滑坡、崩塌灾害的发生看作气象因素和下垫面相互作用结果。其中,气象因素主要为降水,包括降雨诱发作用、降雨滞后作用等因素。利用信息量法,客观反映预警单元的地质条件,分析气象因素对滑坡、崩塌灾害提供的信息量[5]。计算气象因素和地质灾害的相关函数A(y,xi),公式为:

    A(y,xi)=log2B(y|xi),i=1,2,,n (1)

    式中:y——贵州省地质灾害;

    xi——第i种地质灾害相关气象因素;

    n——因素数量。

    xi是与y有关的变量,B(y|xi)为各变量xi条件下的条件概率[6]。计算单个气象因素i的信息量值Ii,公式为:

    A(y,xi)=i=1nIi (2)

    将整个贵州省区域划分为单元网格,利用频率统计,估算条件概率B(y|xi),确定省内地质灾害敏感性[7]。则地质灾害相关气象因素的总信息量值Q,计算公式为:

    Q=i=1nEi/ECi/CIi (3)

    式中:Ei——第i种气象因素引发的地质灾害点数量;

    E——地质灾害点总数;

    Ci——第i种气象因素引发的地质灾害点面积;

    C——贵州省内总面积。

    通过总信息量值,定量分析气象因素对地质灾害的影响程度,Q值为正时,判定气象因素利于地质灾害发生,Q值为负时,则判定不利于灾害发生,且气象因素影响程度大小与Q值大小呈正相关[8]。至此完成气象因素对地质灾害影响程度的计算。

    在确定气象因素对地质灾害影响程度基础上,利用机器学习中的人工神经网络,判断贵州省各区域是否发生地质灾害。首先,参照采集地质环境数据,结合岩性特征、地形地貌、以及气候条件因素,对贵州省各区域进行条件比较,利用GIS空间分析功能,定量划分贵州省地质灾害易发生区,并明确其易发生等级[9]。划分结果见图1

    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

    根据贵州省近20年历史降雨量数据,以及记录的滑坡、崩塌灾害数据,明确各区域的降雨量数据,包含当日临界雨量和5日临界雨量。同时,根据滑坡、崩塌灾害野外调查结果,对临界雨量值进行适当调整,以此减小历史统计数据误差[10]。最后,将预报区域中易发生区等级、实际降雨量、坐标点X坐标和Y坐标,作为机器学习的人工神经网络4个输入节点,利用线性函数,激活神经网络的输入层和输出层,再利用Sigmoid函数,激活隐含层,输入前对数据进行归一化处理,使各数据处于同等水平,消除量纲影响,避开Sigmoid函数训练数据的饱和区[11]。神经网络结构见图2

    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2中,设置网络误差收敛到最小时,其相对应的隐含层节点数为4,将滑坡、崩塌灾害性发生可能性,作为神经网络的1个输出节点。根据机器学习输出结果,判定坐标点区域是否发生滑坡、崩塌灾害,完成滑坡、崩塌灾害发生的判断。

    针对贵州省滑坡、崩塌灾害发生区域,根据气象因素影响程度,计算气象引发因子指数,结合该区域的地质灾害潜势度、承灾体脆弱性,划分气象风险的预警等级。

    对预警区域进行单元编号,计算单元区域j内的有效降雨量Hj,公式为:

    Hj=u=1mFjCjkTju (4)

    式中:m——有效降雨日数;

    u——预警当日向前计算的天数;

    Fj——区域j的当日和5日预报雨量值;

    Cj——当日和5日临界雨量值;

    Tju——区域j固定天数前的降雨量[12-13]

    计算单元区域j的气象引发因子指数Dj,公式为:

    Dj=HjξQ (5)

    式中:ξ——有效降雨系数;

    Q——代表单元区域滑坡、崩塌灾害气象因素分量。

    计算滑坡、崩塌灾害潜势度G,公式为:

    G=d=1hadVd (6)

    式中:h——地质环境因子总个数;

    ad——地质环境因子d的权重;

    Vd——地质环境因子d的量化值。

    采用评价指标方式,在承灾体范围内,提取一级指标和二级指标,计算承灾体脆弱性M,公式为:

    M=r=1Ybrsr (7)

    式中:Y——评价因子总个数;

    br——评价因子r的权重;

    sr——脆弱性评价因子r的量化值。

    气象风险可概化公式为:

    R=M×G×Dj (8)

    其中,R为区域j的气象风险预警指数,取值介于0~1之间,预警指数越大,判定其地质灾害越易发生[14-17]

    其预警级别见表1表3

    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 
    | 显示表格
    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 
    | 显示表格
    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 
    | 显示表格

    在滑坡、崩塌灾害气象风险预警级别中,滑坡、崩塌灾害气象风险预警的等级为:

    (1)蓝色预警(一级):有一定风险,关注降雨;

    (2)黄色预警(二级):风险较高,关注降雨,做好监控;

    (3)橙色预警(三级):风险高,注意降雨,做好监控及应急准备;

    (4)红色预警(四级):风险很高,注意降雨,做好监控与应急撤离准备。

    将气象风险预警指数R,与预警临界值相比较,确定该区域是否发布预警,以及相应的预警级别,完成基于机器学习的贵州省滑坡、崩塌灾害气象风险预警方法设计。

    选取两种常规滑坡、崩塌灾害气象风险预警方法,与此次设计方法进行对比实验,比较各预警等级的空报率大小。

    将滑坡、崩塌灾害,作为贵州省地质灾害研究范围,采集降雨量数据和地质灾害数据,作为实验数据源,在样本中剔除不符合降雨诱发地质灾害个例、以及不匹配区域站降雨资料的降雨量数据。其降雨量历史信息见图3

    图  3  贵州省降水量变化
    Figure  3.  Precipitation change in Guizhou Province

    各区域的当日临界雨量和5日临界雨量,其各级预警的具体数值见表4

    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 
    | 显示表格

    统计可得2014—2020年之间,贵州省地质灾害共发生1204处,发生地质灾害具体数据见表5

    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 
    | 显示表格

    可见滑坡、崩塌占灾害总数的94.9%,三组预警方法分别根据以上历史数据中的滑坡、崩塌灾害,对贵州省地质灾害气象风险进行预警,并以2020年地质灾害作为参照,对比检验三组预警结果。

    2020年崩塌灾害隐患点数量共29处,三组方法均可准确预测出该类地质灾害,其预警等级见图4

    图  4  崩塌预警结果
    Figure  4.  Collapse forecast and early warning results

    图4可知,两组常规方法三级预报数量和四级预警数量要明显多于设计方法,隐患点崩塌预警的严重程度整体偏高。进一步统计所有年份中,各预警级别的空报率,实验对比结果见表6

    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 
    | 显示表格

    表6可知,相比常规方法1和常规方法2,设计方法对崩塌的三级预报空报率分别降低了6.65%和9.65%,四级预警空报率分别降低了6.03%和12.0%。

    2020年滑坡灾害隐患点数量共33处,三组方法都准确预测出该类地质灾害,其预警等级见图5

    图  5  滑坡预警结果
    Figure  5.  Landslide forecast and early warning results

    图5可知,针对滑坡这一地质灾害,两组常规方法的三级预警数量和四级预警数量,同样多于设计方法,隐患点预警的严重程度仍整体偏高。进一步统计所有年份中,各预警级别的空报率,实验对比结果见表7

    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 
    | 显示表格

    表7可知,相比常规方法1和常规方法2,设计方法对滑坡的三级预警空报率分别降低了5.04%和7%,四级预警空报率分别降低了8.51%和11.17%,且常规方法2的二级预警仍存在空报率。

    针对现有滑坡、崩塌地质灾害预警方法中存在的不足,本文提出采用机器学习算法对地质灾害气象风险进行预警的方法。

    (1)该方法通过采用机器学习神经网络中节点的输入,有效预测地质灾害发生。

    (2)采用设计方法与常规方法对比中,本文方法对崩塌预警的三级预报空报率分别降低了5.04%和7%,四级预警空报率分别降低了8.51%和11.17%;对滑坡预警的空报率中,三级预报空报率分别降低了5.04%和7%,四级预警空报率分别降低了8.51%和11.17%,验证了本文方法适用于贵州省滑坡、崩塌灾害气象风险预警。

    但此次研究仍存在一定不足,在今后研究中,会持续验证每年实际发生的地质灾害,修正该模型,进一步提高地质灾害预警精度。

  • 图  1   平山县地理位置

    Figure  1.   Geographical location of Pingshan County

    图  2   石家庄市崩滑流地质灾害隐患点分布

    Figure  2.   Distribution of potential and geological hazard points of collapse, landslide and debris flow in Shijiazhuang

    图  3   平山县灾害点分布

    Figure  3.   Distribution of geological hazard points in Pingshan County

    图  4   地质灾害危险性评价指标体系

    Figure  4.   Geological hazard risk evaluation index system

    图  5   各评价因子分级

    Figure  5.   Classification of each evaluation factor

    图  6   基于AHP平山县地质灾害危险性分区

    Figure  6.   Geological hazard zoning in Pingshan County based on AHP

    图  7   基于突变理论平山县地质灾害危险性分区

    Figure  7.   Geological hazard zoning in Pingshan County based on catastrophe theory

    图  8   基于AHP-突变组合模型平山县地质灾害危险性分区

    Figure  8.   Geological hazard zoning in Pingshan County based on AHP - catastrophe combination model

    表  1   各评价方法求得权重对比

    Table  1   Weight comparison of each evaluation method

    目标层准则层评价因子层AHP权重w1突变理论权重w2AHP-突变组合模型权重w3
    A平山县地质
    灾害危害性评价
    B1 地形地貌C1 地形起伏度0.17610.07950.1359
    C2 坡度0.37310.07170.1880
    C3 坡向0.08150.07950.0925
    C4 河网密度0.03800.08180.0641
    B2 地质构造C5 断裂带密度0.04050.10650.0755
    C6 地层岩性0.20260.24860.2579
    B3 生态环境C7 NDVI0.02490.11820.0624
    C8 土地利用类型0.05670.12200.0956
    C9 灾害点密度0.00650.09210.0281
    下载: 导出CSV

    表  2   状态变量的突变模型

    Table  2   Catastrophe model of state variable

    突变模型控制变量维数势函数归一化公式
    折叠突变113x3+axxa=a
    尖点突变214x4+12ax2+bxxa=axb=b3
    燕尾突变315x5+13ax3+12bx2+cxxa=axb=b3xc=c4
    蝴蝶突变416x6+14ax4+13bx3+12cx2+dxxa=axb=b3xc=c4xd=d5
    下载: 导出CSV

    表  3   危险性分区统计与对比

    Table  3   Risk zoning statistics and comparison

    评价方法危险性等级面积占比/%
    AHP25.14
    33.37
    27.16
    极高14.33
    突变理论13.96
    26.36
    28.16
    极高31.51
    AHP-突变理论组合模型18.39
    32.61
    27.49
    极高21.51
    下载: 导出CSV
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  • 期刊类型引用(1)

    1. 苏娜,徐林荣,李永威,李佳珅,汤玉兰. 汶川地震震后七盘沟泥石流动态物源危险性评价. 中国地质灾害与防治学报. 2025(01): 16-27 . 本站查看

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出版历程
  • 收稿日期:  2022-01-16
  • 修回日期:  2022-04-07
  • 网络出版日期:  2023-01-09
  • 刊出日期:  2023-04-24

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