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基于地貌特征的滑坡堰塞坝形成敏感性研究

黄健 贺子城 黄祥 王豪

黄健, 贺子城, 黄祥, 王豪. 基于地貌特征的滑坡堰塞坝形成敏感性研究[J]. 地质科技通报, 2021, 40(5): 253-262. doi: 10.19509/j.cnki.dzkq.2021.0040
引用本文: 黄健, 贺子城, 黄祥, 王豪. 基于地貌特征的滑坡堰塞坝形成敏感性研究[J]. 地质科技通报, 2021, 40(5): 253-262. doi: 10.19509/j.cnki.dzkq.2021.0040
Huang Jian, He Zicheng, Huang Xiang, Wang Hao. Formation sensitivity of landslide dam based on geomorphic characteristics[J]. Bulletin of Geological Science and Technology, 2021, 40(5): 253-262. doi: 10.19509/j.cnki.dzkq.2021.0040
Citation: Huang Jian, He Zicheng, Huang Xiang, Wang Hao. Formation sensitivity of landslide dam based on geomorphic characteristics[J]. Bulletin of Geological Science and Technology, 2021, 40(5): 253-262. doi: 10.19509/j.cnki.dzkq.2021.0040

基于地貌特征的滑坡堰塞坝形成敏感性研究

doi: 10.19509/j.cnki.dzkq.2021.0040
基金项目: 

国家创新研究群体科学基金项目 41521002

详细信息
    作者简介:

    黄健(1984-), 男, 副教授, 主要从事地质灾害预测预报方面的研究工作。E-mail: hjian.2010@qq.com

    通讯作者:

    贺子城(1997-), 男, 现正攻读地质工程专业硕士学位, 主要从事地质灾害风险评价方面的研究工作。E-mail: hezicheng00@163.com

  • 中图分类号: P642.22

Formation sensitivity of landslide dam based on geomorphic characteristics

  • 摘要: 滑坡堰塞坝是由斜坡失稳堵塞河道而形成的天然坝体,且易溃坝诱发洪水,对沿岸群众生命财产构成巨大的威胁。为提升主动减灾防灾能力,急需构建了一种快速预测与判断滑坡堵江成坝能力的方法。通过文献资料查阅,结合遥感技术,提取了70处典型滑坡的地貌特征参数,其中50处为堵江成坝滑坡。运用K-S检验和M-W U检验方法分析了滑坡地貌特征因子的敏感性,利用Boruta算法确定了因子重要度,筛选了滑坡体积、面积、高差、长度及河宽共5个地貌特征参数。基于此,利用Bayes判别法与逻辑回归方法,分别建立了滑坡堰塞坝形成的预测模型,准确率超过90%。选取高重要度且差异显著的因子,利用比值法建立了滑坡堵江成坝阈值判据,实现了滑坡堰塞坝形成的快速判定。统计不同诱因下滑坡地貌特征,对比V-Wr经验公式,确定了滑坡堰塞坝形成与诱因间的关系,为进一步构建不同诱因下滑坡堰塞坝形成预测模型提供了技术支撑。

     

  • 图 1  地貌特征参数示意图(以白格滑坡为例,具体含义见表 1)

    Figure 1.  Schematic diagram of geomorphic characteristics

    图 2  堵江成坝滑坡地貌特征参数统计分布图

    Figure 2.  Statistical distribution diagram of geomorphic characteristics parameters in damming landslides

    图 3  地貌特征因子重要程度排序

    Figure 3.  Order of importance degree of geomorphic characteristics

    图 4  滑坡高差H与(滑坡长度/河宽)L/Wr的关系

    Figure 4.  Relation between H and L/Wr

    图 5  滑坡高差H与(滑坡长度/河宽)L/Wr的统计分析

    Figure 5.  Statistical analysis of H and L/Wr

    图 6  不同诱因下的滑坡地貌特征参数统计分析

    Figure 6.  Statistics analysis of geomorphic characteristics under different incentives

    图 7  滑坡体积V与河宽Wr的关系

    Figure 7.  Relation between V and Wr

    表  1  地貌特征参数的确定及量值提取方法[3, 5, 7, 10-24]

    Table  1.   Determination of the geomorphologic characteristic parameters and extraction method for the quantitative value

    地貌特征 定义 提取方法
    滑坡面积A/m2 滑坡形成(破坏)和堆积的平面面积 结合文献资料和Google Earth的历史影像信息解译滑坡范围,计算面积;同时可借助经验公式计算体积
    滑坡体积V/104m3 估算发生滑坡的体积
    滑坡高差H/m 滑坡高程的变化量(滑坡后缘与前缘的高程差) 结合文献资料并利用Google Earth历史影像信息与标尺工具提取滑坡高差、长度、宽度和河宽
    滑坡长度L/m 滑坡纵向长度
    滑坡宽度WL/m 滑坡最大的横向宽度
    河宽Wr/m 河道的宽度
    滑坡坡度S/(°) 滑坡体表面与水平方向的夹角,取其平均值为滑坡坡度 利用GIS中3D分析工具与DEM计算坡度、坡向
    H/L比值 HL的比值 根据滑坡高差与长度值,计算H/L
    下载: 导出CSV

    表  2  堵江成坝滑坡的地貌特征参数[2, 4, 6, 10-24]

    Table  2.   Geomorphic characteristics parameters of damming landslide

    编号 诱因 A/m2 V/104m3 H/m L/m H/L S/(°) WL/m Wr/m 资料来源
    1 台风 55 667 31 119 373 0.319 22.5 311 273 Chen等[7]
    2 台风 34 954 16 102 287 0.355 24.0 116 33
    3 台风 61 590 35 263 574 0.458 29.8 349 92
    4 台风 149 377 114 346 608 0.569 41.8 518 24
    5 台风 38 910 19 117 307 0.381 29.7 228 47
    6 台风 83 997 53 294 1 046 0.281 15.7 144 145
    7 台风 324 744 320 429 1523 0.282 27.3 450 173
    8 台风 25 192 11 106 352 0.301 2.5 105 184
    9 台风 329 953 327 597 807 0.740 32.2 556 69
    10 台风 366 400 376 500 919 0.544 24.6 810 188
    11 台风 2 487 075 4 824 621 1 990 0.312 24.7 2 782 16
    12 台风 6 109 2 36 116 0.310 4.5 109 20
    13 台风 61 870 35 53 193 0.275 1.7 438 102
    14 台风 204 452 173 318 1 428 0.223 26.4 322 16
    15 台风 41 3071 441 331 917 0.361 24.7 1 290 265
    16 台风 8 576 3 60 97 0.619 12.3 131 24
    17 台风 87 926 56 289 308 0.938 33.4 280 26
    18 台风 47 884 25 140 678 0.206 15.1 88 28
    19 台风 58 936 33 161 539 0.299 30.4 137 26
    20 地震 26 267 11 197 270 0.730 17.4 165 46
    21 降雨 76 233 46 261 419 0.623 28.6 290 5
    22 台风 20 603 8 193 379 0.509 26.5 267 52
    23 台风 92 679 60 181 451 0.401 32.1 400 16
    24 地震 6 045 884 15 749 822 2 046 0.402 24.9 1 663 194
    25 地震 1 982 762 3 567 505 1 212 0.417 21.9 1 268 38
    26 降雨 165 621 131 406 937 0.433 27.5 205 179
    27 降雨 61 081 35 121 398 0.304 27.7 113 17
    28 降雨 277 593 260 155 350 0.443 36.0 120 55 樊晓一等[10]
    29 地震 1 490 909 2 440 835 1 200 0.696 27 130 611.8 胡卸文等[11]
    30 地震 3 730 000 115 900 1 567 3 400 0.461 29 1 500 170 许强等[3]
    31 降雨 520 000 1 542 300 1 205 0.249 25 445 65 樊晓一等[5]
    32 降雨 1 954 688 3 500 850 1 413 0.602 57.5 560 112 许强等[12]
    33 地震 255 770 350 250 472 0.530 36 308 160 樊晓一等[5]
    34 地震 182 000 468 562 880 0.639 48 400 172
    35 降雨 1 400 000 9 500 1 000 1 500 0.667 60 1 150 200
    36 降雨 251 880 424 430 770 0.558 65 432 150
    37 降雨 1 075 000 2 500 190 725 0.262 15 1 300 90 乔建平等[13]
    38 地震 1 504 264 2 110 552 1317 0.419 35 980 156 龙维[14]
    39 降雨 540 000 540 325 1 350 0.241 21 550 65 简文星等[15]
    40 降雨 10 440 18 108 280 0.386 30 100 10 樊晓一等[16]
    41 地震 1 650 000 15 000 238 800 0.298 22 800 436 柴贺军等[17]
    42 地震 91 185 12 280 450 0.622 43 87 130
    43 地震 1 400 387 10 250 1 395 2136 0.653 65 986 330
    44 地震 192 367 300 430 1 000 0.430 45 120 83 裴向军等[18]
    45 地震 313 917 4 000 509 1 034 0.492 22.5 404 135 杨琴等[19]
    46 地震 750 000 60 000 640 1 300 0.492 35 320 90 王家柱等[20]
    47 降雨 10 725 806 30 000 3 330 8 000 0.416 30 912 327 吕杰堂等[21]
    48 降雨 3 100 000 5 040 400 1 000 0.400 25 900 180 周洪福等[22]
    49 地震 500 000 2 000 440 950 0.463 37.5 900 95 陈丹[23]
    50 地震 360 000 2 800 650 750 0.867 60 500 220 柴贺军等[24]
    下载: 导出CSV

    表  3  Bayes线性判别系数

    Table  3.   Bayes linear discriminace coefficient

    因子 M1 M2 M3
    0 1 0 1 0 1
    滑坡面积A -4.632×10-7 -1.338×10-6 -4.606×10-7 -8.381×10-7 -4.259×10-7 -1.154×10-6
    滑坡体积V -2.773×10-5 0.000 -6.514×10-6 -1.092×10-5 5.399×10-7 -9.188×10-6
    滑坡高差H -0.002 0.004 -0.002 0.001 -0.003 0.001
    滑坡长度L 0.002 0.005 0.001 0.002 0.002 0.002
    河宽Wr 0.008 2.901×10-5 0.010 0.002 0.008 0.001
    常量 -2.784 -2.741 -2.527 -1.507 -2.672 -1.601
    下载: 导出CSV

    表  4  各模型分析结果对比

    Table  4.   Comparison of analysis results of each model

    模型 精确度/% 准确率/% 漏报率/% 误报率/%
    M1 93.9 90.0 8.0 6.1
    M2 82.1 80.0 8.0 17.9
    M3 80.4 77.1 10.0 19.6
    下载: 导出CSV

    表  5  Logistic回归分析分类

    Table  5.   Classification of logistic regression analysis

    实测 预测 准确率/%
    0 1
    0 17 3 85.0
    1 2 48 96.0
    总准确率/% 92.9
    下载: 导出CSV

    表  6  Logistic回归分析分类

    Table  6.   Classification of logistic regression analysis

    实测 预测 准确率/%
    0 1
    0 17 3 85.0
    1 4 46 92.0
    总准确率/% 90.0
    下载: 导出CSV

    表  7  滑坡地貌特征参数[29-30]

    Table  7.   Geomorphic characteristics parameters of landslide

    滑坡 类别 H/m lnH L/m Wr/m ln(L/Wr)
    平溪村 1 144 4.970 414 36 2.442
    二蛮山 0 610 6.413 1 000 1 542 -0.433
    下载: 导出CSV
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