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基于机器学习的滑坡易发性预测建模及其主控因子识别

黄发明 胡松雁 闫学涯 李明 王俊宇 李文彬 郭子正 范文彦

黄发明, 胡松雁, 闫学涯, 李明, 王俊宇, 李文彬, 郭子正, 范文彦. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报, 2022, 41(2): 79-90. doi: 10.19509/j.cnki.dzkq.2021.0087
引用本文: 黄发明, 胡松雁, 闫学涯, 李明, 王俊宇, 李文彬, 郭子正, 范文彦. 基于机器学习的滑坡易发性预测建模及其主控因子识别[J]. 地质科技通报, 2022, 41(2): 79-90. doi: 10.19509/j.cnki.dzkq.2021.0087
Huang Faming, Hu Songyan, Yan Xueya, Li Ming, Wang Junyu, Li Wenbin, Guo Zizheng, Fan Wenyan. Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 79-90. doi: 10.19509/j.cnki.dzkq.2021.0087
Citation: Huang Faming, Hu Songyan, Yan Xueya, Li Ming, Wang Junyu, Li Wenbin, Guo Zizheng, Fan Wenyan. Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models[J]. Bulletin of Geological Science and Technology, 2022, 41(2): 79-90. doi: 10.19509/j.cnki.dzkq.2021.0087

基于机器学习的滑坡易发性预测建模及其主控因子识别

doi: 10.19509/j.cnki.dzkq.2021.0087
基金项目: 

国家自然科学基金项目 41807285

国家重点研发计划项目 2019YFC0605001

中国博士后基金项目 2019M652287

中国博士后基金项目 2020T130274

江西省博士后基金项目 2019KY08

详细信息
    作者简介:

    黄发明(1988—),男,副教授,主要从事滑坡易发性预测研究工作。E-mail: faminghuang@ncu.edu.cn

    通讯作者:

    李文彬(1986—),女,讲师,主要从事滑坡易发性建模研究工作。E-mail: 351113619004@email.ncu.edu.cn

  • 中图分类号: P642.22

Landslide susceptibility prediction and identification of its main environmental factors based on machine learning models

  • 摘要: 不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性的主控因子意义重大。针对上述问题, 以支持向量机(support vector machine, 简称SVM)和随机森林(random forest, 简称RF)为例探讨了基于机器学习的滑坡易发性预测及其不确定性, 创新地提出了"权重均值法"来综合计算出更准确的滑坡主控因子。首先获取陕西省延长县滑坡编录和10类基础环境因子, 将因子频率比值作为SVM和RF的输入变量; 再将滑坡与随机选择的非滑坡样本划分为训练集和测试集, 用训练好的机器学习预测出滑坡易发性并制图; 最后用受试者工作曲线、均值和标准差等来评估建模不确定性, 并计算滑坡主控因子。结果表明: ①机器学习能有效预测出区域滑坡易发性, RF预测的滑坡易发性精度高于SVM, 而其不确定性低于SVM, 但两者的易发性分布规律整体相似; ②权重均值法计算出延长县滑坡主控因子依次是坡度、高程和岩性。实例分析和文献综述显示RF模型相较于其他机器学习模型属于可靠性较高的易发性模型。

     

  • 图 1  机器学习模型预测滑坡易发性的流程图

    Figure 1.  Flowchart of the machine learning model for predicting landslide susceptibility

    图 2  研究区概况及滑坡编录

    Figure 2.  Overview of the study area and landslide catalog

    图 3  延长县滑坡基础环境因子(剖面曲率省略)

    Figure 3.  Basic environmental factors of landslide in Yanchang County

    图 4  滑坡易发性分级图

    Figure 4.  Classification diagram of landslide susceptibility

    图 5  RF和SVM模型预测滑坡易发性的ROC曲线

    Figure 5.  ROC curves of the RF and SVM models predicting landslide susceptibility

    图 6  RF和SVM模型预测滑坡易发性指数分布规律

    Figure 6.  Distribution rule of the RF and SVM models predicting landslide susceptibility indexes

    图 7  滑坡环境因子重要性排名

    Figure 7.  Importance ranking of landslide environmental factor

    图 8  滑坡环境因子平均重要性排名

    Figure 8.  Means importance ranking of landslide environmental factor

    表  1  各基础环境因子的属性区间分级及其频率比值

    Table  1.   Attribute interval and frequency ratio of each evaluation factor

    基础环境因子 变量值 全区栅格/个 栅格比例/% 滑坡栅格/个 滑坡栅格比例/% 频率比
    坡向/(°) (连续型) -1 190 0.007 0 0.000 0.000
    [0, 22.5) 222 188 8.472 211 6.200 0.732
    [22.5, 67.5) 304 138 11.597 576 16.926 1.459
    [67.5, 112.5) 484 681 18.482 481 14.135 0.765
    [112.5, 157.5) 345 859 13.188 636 18.689 1.417
    [157.5, 202.5) 254 226 9.694 430 12.636 1.303
    [202.5, 247.5) 333 472 12.716 451 13.253 1.042
    [247.5, 292.5) 382 265 14.576 241 7.082 0.486
    [292.5, 337.5) 295 463 11.267 377 11.078 0.983
    [337.5, 360] 222 188 8.472 211 6.200 0.732
    坡度/(°) (连续型) [0, 6.10) 262 663 10.016 38 1.117 0.111
    [6.10, 10.45) 420 175 16.022 148 4.349 0.271
    [10.45, 14.20) 485 296 18.505 369 10.843 0.586
    [14.20, 17.55) 477 773 18.218 638 18.748 1.029
    [17.55, 20.70) 417 670 15.927 803 23.597 1.482
    [20.70, 23.86) 309 745 11.811 741 21.775 1.844
    [23.86, 27.61) 186 825 7.124 540 15.868 2.227
    [27.61, 50.29] 62 335 2.377 126 3.703 1.558
    平面曲率(连续型) [0, 9.91) 531 824 20.279 1028 30.209 1.490
    [9.91, 18.21) 536 386 20.453 938 27.564 1.348
    [18.21, 27.48) 415 938 15.860 563 16.544 1.043
    [27.48, 37.39) 298 275 11.374 338 9.932 0.873
    [37.39, 47.93) 229 817 8.763 194 5.701 0.651
    [47.93, 59.12) 188 588 7.191 154 4.525 0.629
    [59.12, 70.62) 160 843 6.133 78 2.292 0.374
    [70.62, 81.49] 260 811 9.945 110 3.232 0.325
    剖面曲率(连续型) [0, 2.46) 518 211 19.760 691 20.306 1.028
    [2.46, 4.34) 614 610 23.436 839 24.655 1.052
    [4.34, 6.33) 531 797 20.278 736 21.628 1.067
    [6.33, 8.33) 377 404 14.391 492 14.458 1.005
    [8.33, 10.44) 264 686 10.093 294 8.639 0.856
    [10.44, 12.90) 181 130 6.907 223 6.553 0.949
    [12.90, 15.95) 99 956 3.812 94 2.762 0.725
    [15.95, 29.90] 34 688 1.323 34 0.999 0.755
    高程/m (连续型) [473.14, 656.00) 65 566 2.500 0 0.000 0.000
    [656.00, 772.04) 162 922 6.213 0 0.000 0.000
    [772.04, 866.99) 282 304 10.765 336 9.874 0.917
    [866.99, 944.35) 422 849 16.124 1 078 31.678 1.965
    [944.35, 1 014.68) 552 133 21.054 1 068 31.384 1.491
    [1 014.68, 1 085.01) 551 281 21.021 605 17.778 0.846
    [1 085.01, 1 165.89) 395 921 15.097 207 6.083 0.403
    [1 165.89, 1 369.84] 189 506 7.226 109 3.203 0.443
    NDVI (连续型) [0.054, 0.161) 4 323 0.165 0 0.000 0.000
    [0.161, 0.222) 222 528 8.485 346 10.167 1.198
    [0.222, 0.248) 347 566 13.253 503 14.781 1.115
    [0.248, 0.271) 522 175 19.911 590 17.338 0.871
    [0.271, 0.290) 651 814 24.855 837 24.596 0.990
    [0.290, 0.316) 726 302 27.695 958 28.152 1.016
    [0.316, 0.514) 147 748 5.634 169 4.966 0.881
    [0.514, 0.880] 26 0.001 0 0.000 0.000
    NDBI (连续型) [0.015, 0.032) 166 0.006 0 0.000 0.000
    [0.032, 0.523) 20 500 0.782 3 0.088 0.113
    [0.523, 0.550) 144 671 5.517 263 7.728 1.401
    [0.550, 0.569) 328 825 12.539 477 14.017 1.118
    [0.569, 0.585) 461 789 17.609 517 15.192 0.863
    [0.585, 0.601) 638 451 24.345 610 17.925 0.736
    [0.601, 0.617) 636 591 24.274 816 23.979 0.988
    [0.617, 0.701] 391 489 14.928 717 21.070 1.411
    MNDWI (连续型) [0.192, 0.328) 414 411 15.802 749 22.010 1.393
    [0.328, 0.356) 732 165 27.919 866 25.448 0.912
    [0.356, 0.384) 565 063 21.547 559 16.427 0.762
    [0.384, 0.418) 421 031 16.055 479 14.076 0.877
    [0.418, 0.455) 292 549 11.155 421 12.371 1.109
    [0.455, 0.513) 192 388 7.336 329 9.668 1.318
    [0.513, 0.640) 4 737 0.181 0 0.000 0.000
    [0.640, 0.981] 138 0.005 0 0.000 0.000
    总辐射(连续型) [0, 90) 6 815 0.260 0 0.000 0.000
    [90, 170) 68 498 2.612 98 2.880 1.103
    [170, 185) 187 607 7.154 456 13.400 1.873
    [185, 198) 276 782 10.554 448 13.165 1.247
    [198, 211) 339 667 12.952 507 14.899 1.150
    [211, 225) 424 230 16.177 589 17.308 1.070
    [225, 239) 537 676 20.503 567 16.662 0.813
    [239, 255] 781 207 29.789 738 21.687 0.728
    岩性(离散型) 泥岩和油页岩 179 598 6.848 0 0.000 0.000
    单独泥岩 140 332 5.351 364 10.696 1.999
    砂岩与泥岩 134 435 5.126 425 12.489 2.436
    石英砂岩 2 231 0.085 0 0.000 0.000
    风积和洪积黄土 2 165 886 82.589 2 614 76.815 0.930
    下载: 导出CSV

    表  2  RF和SVM模型易发性图的频率比精度分析

    Table  2.   Frequency ratio precision analysis of susceptibility graphs of RF and SVM models

    模型 易发性等级 全区栅格/个 全区栅格比例/% 滑坡栅格/个 滑坡栅格比例/% 频率比(FR)
    RF 极低 695 561 26.52 9 0.26 0.010
    641 183 24.45 39 1.15 0.047
    575 729 21.95 153 4.50 0.205
    428 719 16.35 478 14.05 0.859
    极高 281 290 10.73 2 724 80.05 7.463
    SVM 极低 773 173 29.48 84 2.47 0.084
    699 525 26.67 346 10.17 0.381
    388 165 14.80 357 10.49 0.709
    337 687 12.88 568 16.69 1.296
    极高 423 932 16.17 2 048 60.18 3.723
    下载: 导出CSV
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