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基于RSIV-RF模型的凉山州泥石流易发性评价

饶姗姗 冷小鹏

饶姗姗, 冷小鹏. 基于RSIV-RF模型的凉山州泥石流易发性评价[J]. 地质科技通报, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267
引用本文: 饶姗姗, 冷小鹏. 基于RSIV-RF模型的凉山州泥石流易发性评价[J]. 地质科技通报, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267
RAO Shanshan, LENG Xiaopeng. Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267
Citation: RAO Shanshan, LENG Xiaopeng. Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model[J]. Bulletin of Geological Science and Technology, 2024, 43(1): 275-287. doi: 10.19509/j.cnki.dzkq.tb20220267

基于RSIV-RF模型的凉山州泥石流易发性评价

doi: 10.19509/j.cnki.dzkq.tb20220267
基金项目: 

四川省科技厅应用基础研究项目 2021YJ0335

四川省高校气象灾害预测预警研究项目 ZHYJ21-ZC01

详细信息
    作者简介:

    饶姗姗, E-mail: 943378644@qq.com

    通讯作者:

    冷小鹏, E-mail: lengxiaopeng@cdut.edu.cn

  • 中图分类号: P642.23

Debris flow susceptibility evaluation of Liangshan Prefecture based on the RSIV-RF model

More Information
  • 摘要:

    针对随机森林(RF)模型进行泥石流易发性评价过程中存在连续型因子依靠主观意识分级、随机选取的非泥石流样本准确度较低等问题,以位于四川西南部的凉山彝族自治州为研究区,提出基于统计学先验模型抽样的随机森林对研究区进行泥石流易发性评价分区。利用累计灾害频率等曲线的相对变化对连续型因子进行分级处理;采用粗糙集理论(RS)和信息量法(Ⅳ)计算加权信息量值,划定极低和低易发性区并从中选择负样本数据。通过袋外误差(OOB)变化曲线确定RF模型的最佳树棵数n_estimators和分裂特征数max_features,随后构建加权信息量-随机森林(RSIV-RF)模型预测凉山州泥石流易发性。进一步地,与从全区随机选择非泥石流样本的RF模型开展对比研究。结果表明,训练集和测试集下RSIV-RF模型的准确度分别为0.89,0.83,且对应的ROC曲线的AUC值分别为0.920,0.895,均高于单独的RF模型;RSIV-RF绘制的泥石流易发性评价图与历史灾害分布较为一致,较高和高易发性等级区域占研究区面积比为18.625%,包含了78.57%的泥石流点。性能评估和易发性统计结果均表明基于RSIV-RF能够解决单独模型存在的非泥石样本采样不准确的问题,其泥石流易发性预测精度更高,在凉山州地区泥石流易发性评价研究中具有较好的适应性。

     

  • 图 1  凉山州泥石流易发性评价流程

    Figure 1.  Evaluation process of debris flow susceptibility in Liangshan Prefecture

    图 2  研究区凉山州地理位置及灾害分布

    a.研究区位置; b.研究区行政区划; c.基本地理情况

    Figure 2.  Geological location and disaster distribution in Liangshan Prefecture

    图 3  连续性型因子分级曲线

    a.高程; b.坡度; c.坡向; d.归一化植被指数; e.道路距离; f.水系距离; g.降雨量; h.土地利用

    Figure 3.  Evaluation factor status grading curve

    图 4  RSIV负样本分布

    Figure 4.  RSIV negative sample distribution

    图 5  随机森林的袋外误差曲线

    Figure 5.  Out-of-pocket error curve of random forest

    图 6  RSIV-RF研究区泥石流易发性评价图

    Figure 6.  Assessment map of debris flow susceptibility in the RSIV-RF study area

    图 7  2种模型训练集和测试集下的AUC曲线

    Figure 7.  AUC curves of the two model training sets and test sets

    表  1  指标相关性系数计算

    Table  1.   Calculation of the index correlation coefficient

    指标因子 降雨量 坡度 距道路距离 距河流距离 坡向 高程 土地利用 NDVI
    降雨量 1.000
    坡度 0.101 1.000
    距道路距离 -0.110 -0.020 1.000
    距河流距离 0.003 -0.011 0.340 1.000
    坡向 -0.123 -0.113 0.098 -0.125 1.000
    高程 -0.486 -0.102 0.447 0.301 0.113 1.000
    土地利用 -0.057 0.037 0.140 -0.062 0.115 0.119 1.000
    NDVI 0.124 0.304 0.058 0.132 -0.009 -0.044 -0.034 1.000
    下载: 导出CSV

    表  2  指标因子RS权重系数计算

    Table  2.   Calculation of the index factor RS weight coefficient

    指标因子 属性重要度 权重系数 权重大小排序
    降雨量 0.065 5 0.104 8 6
    坡度 0.093 3 0.149 2 3
    距道路距离 0.055 6 0.088 9 7
    距河流距离 0.049 6 0.079 4 8
    坡向 0.117 1 0.187 3 1
    高程 0.103 2 0.165 1 2
    土地利用 0.071 4 0.114 3 4
    NDVI 0.069 4 0.111 1 5
    下载: 导出CSV

    表  3  指标因子加权信息量计算结果

    Table  3.   Calculation results of weighted information content of index factors

    指标因子 二级状态 信息量值 加权信息量值
    降雨量/mm 900 -1.492 5 -0.156 4
    950 -2.644 4 -0.277 0
    1050 -0.136 6 -0.014 3
    1150 0.179 1 0.018 8
    >1150 1.549 3 0.162 3
    坡度/(°) [0, 15) -0.502 4 -0.075 0
    [15, 20) 0.196 1 0.029 3
    [20, 25) 0.431 7 0.064 4
    [25, 45) -0.272 6 -0.040 7
    [45, 60] -0.779 1 -0.116 2
    道路距离/m [0, 300) 2.126 7 0.189 0
    [300, 700) 0.868 6 0.077 2
    [700, 1 000) -0.114 5 -0.010 2
    [1 000, 1500) 0.728 4 0.064 7
    [1 500, 2 000] -0.344 8 -0.030 6
    >2 000 -0.461 9 -0.041 1
    河流距离/m [0, 200) 1.631 7 0.129 5
    [200, 300) 2.012 7 0.159 7
    [300, 600) 1.458 9 0.115 8
    [600, 1 000) 1.360 6 0.108 0
    [1 000, 1 500) 0.641 1 0.050 9
    [1 500, 2 000] 0.081 5 0.006 5
    >2 000 -0.539 5 -0.042 8
    坡向 -1 0.000 0 0.000 0
    N-NE 0°~67.5° -0.379 8 -0.071 1
    E-S 67.5°~202.5° 0.099 3 0.018 6
    SW-W 202.5°~292.5° 0.161 0 0.030 1
    WE-N 292.5°~360° -0.154 7 -0.029 0
    归一植被指数(NDVI) 0.1 1.146 0 0.127 3
    0.2 1.024 0 0.113 8
    0.4 0.564 8 0.062 8
    0.6 0.302 3 0.033 6
    >0.6 -1.137 3 -0.126 4
    土地利用 耕地 0.903 0 0.103 2
    林地 -0.696 0 -0.079 5
    草地 0.038 5 0.004 4
    水域 0.552 9 0.063 2
    建筑用地 1.390 8 0.159 0
    未利用土地 0.000 0 0.000 0
    高程/m 1220 2.090 9 0.345 2
    1630 0.993 0 0.163 9
    2000 0.798 0 0.131 7
    2600 -0.117 7 -0.019 4
    3000 -1.321 2 -0.218 1
    >3000 -2.608 9 -0.430 7
    下载: 导出CSV

    表  4  RSIV-RF模型易发性分区结果统计

    Table  4.   Statistics of susceptibility zoning results of the RSIV-RF model

    易发性分区 栅格数 占栅格比例/% 泥石流数 泥石流比例/% 灾害密度
    极低易发区 36 061 783 54.316 6 2.381 0.044
    低易发区 8 153 595 12.281 16 6.349 0.517
    中易发区 10 288 057 15.496 32 12.698 0.819
    较高易发区 8 104 859 12.208 87 34.524 2.828
    高易发区 4 021 161 6.057 111 44.048 7.273
    下载: 导出CSV

    表  5  RF模型和RSIV-RF模型的评价性能统计

    Table  5.   Evaluation performance statistics of the RF model and RSIV-RF model

    数据集 准确率 均方误差 Kappa系数
    RF RSIV-RF RF RSIV-RF RF RSIV-RF
    训练集 0.87 0.89 0.12 0.10 0.62 0.70
    测试集 0.76 0.83 0.23 0.16 0.55 0.63
    下载: 导出CSV
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  • 收稿日期:  2022-06-13
  • 录用日期:  2022-08-26
  • 修回日期:  2022-08-24

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