Formation sensitivity of landslide dam based on geomorphic characteristics
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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。Abstract: Landslide dams formed by unstable slope usually blocks the river, and later fail to trigger huge floods threatening the downstream residents' lives and properties.In order to improve the ability of active disaster reduction and prevention, it is urgent to construct a method to quickly predict and judge the ability of landslide to build dams.This paper firstly extracts the geomorphic characteristic parameters of 70 typical landslides based on literature reading and remote sensing, including 50 damming landslides.The five parameters (landslide volume, area, relief, length and river width) are determined by K-S test and M-W U test, and the Boruta algorithm is used to determine the factor importance.Then, the Bayes discriminance method and logistic regression method are used to build a prediction model of the landslide dam, in which the accuracy exceeds 90%.The threshold equation combined with H and L/Wr is determined by using factors with high importance and significant differences and the ratio method in order to carry out a rapid judgment of landslide dams formation.The statistic analysis of the landslide geomorphic characteristics of different triggering factors and the prediction result of V-Wr empirical formula determine the relationship between landslide dams formation and triggering factors, providing technical support for the further construction of prediction models for the formation of landslide dams with different inducements.
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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 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
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 H和L的比值 根据滑坡高差与长度值，计算H/L 
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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]
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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
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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
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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
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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
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