LANDSLIDE SUSCEPTIBILITY ASSESSMENT FOR ENSHI, HUBEI PROVINCE: With GIS-based certainty factor and certainty factor-logistic regression coupling model
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摘要:
湖北省恩施州内地质条件复杂, 境内地质灾害数量众多, 尤以滑坡为甚. 以该州为研究范围, 择取了包括地表坡度、斜坡坡型、坡向、构造、道路、地表水系、地层岩性、植被覆盖率8个方面的影响因素, 基于ArcGIS平台统计分析空间数据的功能, 分别采用确定系数模型及确定系数和逻辑回归耦合模型的方法进行区域滑坡地质灾害易发性评价, 再通过对验证集灾害点在各个分区内的遍布情况和AUC值的比对进行两种模型的精度验证. 结果表明两种模型易发性分区结果大体上一致, 耦合模型的精度略高一筹. 基于该组合模型计算出的易发值, 将恩施州滑坡灾害易发性等级划为低易发区、中易发区、高易发区和极高易发区, 为该地区地质灾害防治提供支撑.
Abstract:The geological conditions in Enshi Prefecture of Hubei Province are complex, with a high number of geological disasters, especially landslides. Taking the prefecture as the research area, eight influencing factors are selected, including surface slope, slope type, slope aspect, structure, road, water system, formation lithology and vegetation coverage. Based on the statistical analysis of spatial data on ArcGIS platform, the regional landslide susceptibility is evaluated by using the certainty factor (CF) and certainty factor-logistic regression (CF-LR) coupling model. Then the accuracy of both models is verified by comparing the distribution of disaster sites in the validation set in each zoning area and AUC values. The results show that the susceptibility zoning of the two models are generally consistent, although the accuracy of the coupling model is slightly higher. Based on the calculated values of the combined models, Enshi area is classified into low, medium, high and extra-high susceptible zones in terms of landslide susceptibility level, which can provide support for the prevention and control of geological hazards in the area.
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Key words:
- geological disaster /
- landslide /
- susceptibility assessment /
- certainty factor /
- logistic regression /
- Hubei Province
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表 1 易发性评价因子相关性矩阵
Table 1. Correlation matrix of susceptibility evaluation factors
因子图层 高差 坡向 坡度 斜坡坡型 植被覆盖率 地层岩性 断层 道路 水系 高差 1 坡向 0.01313 1 坡度 0.92533 0.02329 1 斜坡坡型 0.01085 0.02547 0.02339 1 植被覆盖率 0.17952 0.00423 0.17875 0.03787 1 地层岩性 0.02632 0.01724 0.02218 0.00610 0.01497 1 断层 0.00560 -0.00196 -0.00225 0.00408 0.00416 -0.05787 1 道路 0.17085 -0.00548 0.15462 0.00536 0.13018 -0.01968 0.04191 1 水系 -0.04701 0.07083 -0.03119 0.02910 0.20345 0.09732 0.0.953 0.12175 1 
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表 2 评价因子分级CF值
Table 2. CF values for susceptibility evaluation factors of different levels
评价因子 因子分级 分级面积/km2 总面积/km2 灾害点数量 CF 评价因子 因子分级 分级面积/km2 总面积/km2 灾害点数量 CF 坡度/（°） 0~10 5540.77 24114.61 346 -0.284964 构造缓冲区/m ＜200 1130.32 24114.61 109 0.253111 10~20 8734.40 24114.61 862 0.115080 200~400 1858.81 24114.61 232 0.290977 20~30 6237.48 24114.61 657 0.170872 400~800 2390.85 24114.61 245 0.116375 30~40 2759.73 24114.61 200 -0.170179 800~1 200 2665.46 24114.61 254 0.044571 40~50 716.21 24114.61 40 -0.360499 1 200~1 600 449.68 24114.61 24 -0.320114 >50 126.02 24114.61 1 -0.909141 >1 600 15619.49 24114.61 1242 -0.092267 坡向 北 2714.20 24114.61 240 0.012337 水系缓冲区/m ＜100 2035.76 24114.61 337 0.556090 北东 2518.64 24114.61 192 -0.127117 100~200 1862.31 24114.61 335 0.501733 东 3183.82 24114.61 266 -0.043347 200~400 940.35 24114.61 188 0.526812 南东 2591.82 24114.61 179 -0.209194 400~600 545.75 24114.61 87 0.446493 南 2879.75 24114.61 271 0.071968 600~800 90.50 24114.61 18 0.651735 南西 3726.70 24114.61 354 0.080610 >800 18639.94 24114.61 1141 -0.326288 西 3052.96 24114.61 282 0.054526 地层岩性 Ⅰ 69.93 24114.61 11 0.444838 北西 3446.71 24114.61 322 0.065182 Ⅱ-1 385.41 24114.61 29 -0.13843 植被覆盖率 极低 92.34 24114.61 13 0.379641 Ⅱ-2 77.54 24114.61 7 0.032569 低 197.26 24114.61 42 0.589821 Ⅱ-3 329.75 24114.61 35 0.177189 中 1275.49 24114.61 258 0.568245 Ⅱ-4 1062.29 24114.61 90 -0.029889 高 4836.33 24114.61 845 0.500153 Ⅱ-5 4818.06 24114.61 446 0.056558 极高 17713.18 24114.61 948 -0.387179 Ⅱ-6 1591.50 24114.61 454 0.693853 道路缓冲区/m ＜100 873.59 24114.61 185 0.587606 Ⅲ 3538.93 24114.61 380 0.186670 100~200 1319.42 24114.61 309 0.627091 Ⅳ-1 6411.62 24114.61 340 -0.392798 200~400 2533.54 24114.61 302 0.267347 Ⅳ-2 5749.23 24114.61 313 -0.376615 400~600 1746.53 24114.61 165 0.075578 Ⅴ 80.33 24114.61 1 -0.857466 600~800 976.43 24114.61 111 0.231764 斜坡坡型 凸型 10554.51 24114.61 984 0.063255 800~1 200 431.59 24114.61 27 -0.283672 直线和阶梯型 3543.94 24114.61 350 0.115706 1 200~1 600 402.40 24114.61 13 -0.630077 凹型坡 10016.01 24114.61 772 -0.117440 >1 600 15831.12 24114.61 994 -0.281053 注：Ⅰ—第四系松散松软土类；Ⅱ-1—中－厚层状坚硬石英岩、石英砂岩岩组；Ⅱ－2—块状－厚层状较坚硬砂岩、砾岩岩组；Ⅱ－3—厚层状坚硬、较坚硬泥砾岩岩组；Ⅱ-4—薄－厚层状较坚硬至软弱砂岩、泥质粉砂岩夹长石石英砂岩、页岩，煤层与泥岩、页岩互层岩组；Ⅱ－5—薄层－中厚层状软弱页岩、粉砂岩、泥岩岩组；Ⅱ-6—薄层－中厚层状软弱泥质粉砂岩、页岩岩组；Ⅲ—薄－中厚层状坚硬、较坚硬泥灰岩、灰岩、瘤状灰岩、硅质岩、龟裂纹灰岩夹软弱页岩、泥岩、炭质页岩及煤层或瘤状灰岩与页岩互层岩组；Ⅳ－1—薄－厚层状坚硬灰岩、白云岩、白云质灰岩、灰质白云岩、泥质白云岩岩组；Ⅳ－2—中－厚层状坚硬、较坚硬灰岩、燧石结核灰岩岩组；Ⅴ—中－厚层块状坚硬白云岩夹白云质粉砂岩岩组.
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表 3 基于CF模型的逻辑回归分析结果
Table 3. Results of logistic regression analysis based on CF model
回归项 B SE wals df sig 坡度 1.801 0.209 7.397 1 0.000 构造 2.379 0.300 6.293 1 0.000 地表水系 2.231 0.120 3.476 1 0.000 道路 1.122 0.084 1.788 1 0.001 地层岩性 2.398 0.131 3.348 1 0.000 植被覆盖率 2.145 0.105 4.180 1 0.003 斜坡坡型 0.210 0.002 0.170 1 0.001 坡向 1.630 0.428 14.511 1 0.000 常量 1.071 0.059 3.387 1 0.000 注: B为各因子回归系数, SE为标准误差, wals为卡方值, df为自由度, sig为显著性.
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表 4 易发性分区结果
Table 4. Results of susceptibility zoning
易发性分区 CF模型 CF-LR模型 低易发区 30.27 40.35 中易发区 34.75 32.57 高易发区 24.84 13.62 极高易发区 10.15 14.35 注: 数值为各分区面积占比(%).
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表 5 研究区滑坡易发分区的检验结果
Table 5. Test results of landslide susceptibility zoning in the study area
易发性分区 CF模型 CF-LR模型 面积占比Si/% 灾害点比例Gi /% Ri 面积占比Si /% 灾害点比例Gi /% Ri 低易发区 30.27 7.02 0.23 40.35 6.45 0.16 中易发区 34.75 21.44 0.62 32.57 18.60 0.57 高易发区 24.84 31.50 1.27 13.62 32.07 2.35 极高易发区 10.15 40.04 3.95 14.35 42.88 2.99
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[1] 王毅, 方志策, 牛瑞卿, 等. 基于深度学习的滑坡灾害易发性分析[J]. 地球信息科学学报, 2021, 23(12): 2244-2260. doi: 10.12082/dqxxkx.2021.210057
Wang Y, Fang Z C, Niu R Q, et al. Landslide susceptibility analysis based on deep learning[J]. Journal of Geo-Information Science, 2021, 23(12): 2244-2260. doi: 10.12082/dqxxkx.2021.210057
[2] 许冲, 戴福初, 姚鑫, 等. 基于GIS与确定性系数分析方法的汶川地震滑坡易发性评价[J]. 工程地质学报, 2010, 18(1): 15-26.
Xu C, Dai F C, Yao X, et al. GIS platform and certainty factor analysis method based Wenchuan earthquake-induced landslide susceptibility evaluation[J]. Journal of Engineering Geology, 2010, 18(1): 15-26.
[3] 张俊, 殷坤龙, 王佳佳, 等. 三峡库区万州区滑坡灾害易发性评价研究[J]. 岩石力学与工程学报, 2016, 35(2): 284-296.
Zhang J, Yin K L, Wang J J, et al. Evaluation of landslide susceptibility for Wanzhou district of Three Gorges Reservoir[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(2): 284-296.
[4] 吴润泽, 胡旭东, 梅红波, 等. 基于随机森林的滑坡空间易发性评价: 以三峡库区湖北段为例[J]. 地球科学, 2021, 46(1): 321-330.
Wu R Z, Hu X D, Mei H B, et al. Spatial susceptibility assessment of landslides based on random forest: A case study from Hubei section in the Three Gorges Reservoir area[J]. Earth Science, 2021, 46(1): 321-330.
[5] 牛瑞卿, 彭令, 叶润青, 等. 基于粗糙集的支持向量机滑坡易发性评价[J]. 吉林大学学报(地球科学版), 2012, 42(2): 430-439.
Niu R Q, Peng L, Ye R Q, et al. Landslide susceptibility assessment based on rough sets and support vector machine[J]. Journal of Jilin University (Earth Science Edition), 2012, 42(2): 430-439.
[6] 黄发明, 殷坤龙, 蒋水华, 等. 基于聚类分析和支持向量机的滑坡易发性评价[J]. 岩石力学与工程学报, 2018, 37(1): 156-167.
Huang F M, Yin K L, Jiang S H, et al. Landslide susceptibility assessment based on clustering analysis and support vector machine [J]. Chinese Journal of Rock Mechanics and Engineering, 2018, 37(1): 156-167.
[7] 罗路广, 裴向军, 黄润秋, 等. GIS支持下CF与Logistic回归模型耦合的九寨沟景区滑坡易发性评价[J]. 工程地质学报, 2021, 29(2): 526-535.
Luo L G, Pei X J, Huang R Q, et al. Landslide susceptibility assessment in Jiuzhaigou scenic area with GIS based on certainty factor and logistic regression model[J]. Journal of Engineering Geology, 2021, 29(2): 526-535.
[8] Shortliffe E H, Buchanan B G. A model of inexact reasoning in medicine[J]. Mathematical Biosciences, 1975, 23(3/4): 351-379.
[9] 栗泽桐, 王涛, 周杨, 等. 基于信息量、逻辑回归及其耦合模型的滑坡易发性评估研究: 以青海沙塘川流域为例[J]. 现代地质, 2019, 33(1): 235-245.
Li Z T, Wang T, Zhou Y, et al. Landslide susceptibility assessment based on information value model, logistic regression model and their integrated model: A case in Shatang River Basin, Qinghai Province [J]. Geoscience, 2019, 33(1): 235-245.
[10] Yin K L, Yan T Z. Statistical prediction models for slope instability of metamorphosed rocks[C] //Proceedings of the 5th International Symposium on Landslides. Lausanne, 1988.
[11] Zhao L R, Wu X L, Niu R Q, et al. Using the rotation and random forest models of ensemble learning to predict landslide susceptibility [J]. Geomatics, Natural Hazards and Risk, 2010, 11(1): 1542-1564.
[12] 焦伟之, 张明, 谢鑫鹏, 等. 基于GIS与加权信息量模型的城镇地质灾害易发性评价——以大新镇为例[J]. 安全与环境工程, 2022, 29(4): 119-128.
Jiao W Z, Zhang M, Xie X P, et al. Susceptibility evaluation of urban geological disaster based on GIS and weighted information value model: A case study of Daxin Town[J]. Safety and Environmental Engineering, 2022, 29(4): 119-128.
[13] 胡涛, 樊鑫, 王硕, 等. 基于逻辑回归模型和3S技术的思南县滑坡易发性评价[J]. 地质科技通报, 2020, 39(2): 113-121.
Hu T, Fan X, Wang S, et al. Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology[J]. Bulletin of Geological Science and Technology, 2020, 39(2): 113-121.
[14] Chen L X, van Westen C J, Hussin H, et al. Integrating expert opinion with modeling for quantitative multi-hazard risk assessment in the Eastern Italian Alps[J]. Geomorphology, 2016, 273: 150-167.
[15] 霍艾迪, 张骏, 卢玉东, 等. 地质灾害易发性评价单元划分方法——以陕西省黄陵县为例[J]. 吉林大学学报(地球科学版), 2011, 41(2): 523-528, 535.
Huo A D, Zhang J, Lu Y D, et al. Method of classification for susceptibility evaluation unit for geological hazards: A case study of Huangling County, Shaanxi, China[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(2): 523-528, 535.
[16] 解明礼, 巨能攀, 赵建军, 等. 区域地质灾害易发性分级方法对比分析研究[J]. 武汉大学学报(信息科学版), 2021, 46(7): 1003-1014.
Xie M L, Ju N P, Zhao J J, et al. Comparative analysis on classification methods of geological disaster susceptibility assessment[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1003-1014.
[17] Zhang Y E, Li D Y, Yin K L, et al. The July 1, 2017 Wangjiawan landslide in Ningxiang County, China[J]. Landslides, 2018, 15(8): 1657-1662.
[18] 刘月, 王宁涛, 周超, 等. 基于ROC曲线与确定性系数法集成模型的三峡库区奉节县滑坡易发性评价[J]. 安全与环境工程, 2020, 27(4): 61-70.
Liu Y, Wang N T, Zhou C, et al. Evaluation of landslide susceptibility based on ROC and certainty factor method in Fengjie County, Three Gorges Reservoir[J]. Safety and Environmental Engineering, 2020, 27(4): 61-70.
[19] 樊芷吟, 苟晓峰, 秦明月, 等. 基于信息量模型与Logistic回归模型耦合的地质灾害易发性评价[J]. 工程地质学报, 2018, 26(2): 340-347.
Fan Z Y, Gou X F, Qin M Y, et al. Information and logistic regression models based coupling analysis for susceptibility of geological hazards [J]. Journal of Engineering Geology, 2018, 26(2): 340-347.
[20] Chen L X, Guo Z Z, Yin K L, et al. The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan'en County (Hubei, China)[J]. Natural Hazards and Earth System Sciences, 2019, 19(10): 2207-2228.
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