Comparative analysis of landslide susceptibility evaluation models based on coefficient of determination method：A case study of Baoshan Basin, Yunnan Province
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摘要:
随着保山盆地城市化进程的不断推进，当地地质条件逐渐恶化，滑坡灾害频发，为了提供切合实际的防治建议，文章对研究区进行滑坡易发性评价。据区域地质背景、人类活动及灾害发育特征，选取海拔、坡度、坡向、归一化植被覆盖度、工程地质岩组、距道路距离、距断层距离、距水系距离、灾害点密度共9个评价因子。建立确定性系数法模型（CF）、确定性系数法与层次分析法耦合模型（CF-AHP）、确定性系数法与熵指数法耦合模型（CF-IOE）及确定性系数法与距离函数法-组合权重耦合模型（CF-AHP-IOE）。结果显示：CF、CF-AHP、CF-IOE、CF-AHP-IOE模型滑坡易性发分级的灾害密度及频率比值显著增加，均有效对研究区进行滑坡易发性评价；4种模型AUC值分别为0.890、0.911、0.921、0.916，耦合模型具有更高评价精度；其中CF-AHP-IOE模型在极高易发区频率比值与灾害数量占比90%以上，说明了主、客赋权更加合理。研究可为保山盆地城市化进程提供地质灾害防治建议。
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关键词:
- 滑坡易发性评价 /
- 确定性系数法 /
- 层次分析法 /
- 熵指数法 /
- 距离函数-组合权重法
Abstract:As urbanization continues to progress in the Baoshan Basin, geological conditions have deteriorated and leading to frequent landslide natural hazards. To provide prevention and control recommendations, landslide susceptibility assessment was carried out in the study area. Nine evaluation factors, including elevation, slope, slope direction, normalized vegetation cover (NDVI), engineering geological rock group, distance from roads, distance from faults, distance from water systems, and density of disaster sites, were selected based on the regional geological background, human activities and disaster development characteristics. Four models were established: deterministic coefficient method (CF), the coupled model of deterministic coefficient method and hierarchical analysis method (CF-AHP), the coupled model of deterministic coefficient method and index of entropy method (CF-IOE), and the coupled model of deterministic coefficient method and distance function-combined weights method (CF-AHP-IOE). The results show that the hazard density and frequency ratios of CF, CF-AHP, CF-IOE, and CF-AHP-IOE models landslide susceptibility grading have significantly increased, effectively assessing the landslide susceptibility in the study area. The AUC values of the three models were 0.890, 0.911, 0.921, and 0.916, respectively, indicating higher evaluation accuracy for the coupled models. Among them, the CF-AHP-IOE model has a ratio of disaster frequency and disaster quantity exceeding 90% in the extremely high susceptibility zone, demonstrating a more reasonable weighting approach. The study can provide preemptive recommendations for the urbanization process in the Baoshan Basin.
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表 1 评价指标因子相关系数矩阵
Table 1. Matrix of correlation coefficients of evaluation indicator factors
评价因子 海拔 坡度 坡向 NDVI 工程地质岩组 道路 断层 水系 灾害点密度 海拔 1 0.471 −0.017 0.412 0.272 0.325 −0.100 −0.162 −0.132 坡度 1 −0.001 0.402 0.269 0.070 −0.188 −0.382 0.008 坡向 1 −0.088 0.127 −0.025 −0.042 0.062 0.020 NDVI 1 0.203 0.159 −0.150 −0.264 −0.018 工程地质岩组 1 −0.061 −0.259 −0.252 0.176 道路 1 0.030 0.162 −0.146 断层 1 0.227 −0.156 水系 1 −0.126 灾害点密度 1 
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表 2 评价因子判断矩阵及权重值
Table 2. Judgment matrix and weight values of evaluation factors
评价因子 海拔 坡度 坡向 归一化植被覆盖度 工程地质岩组 道路 断层 水系 灾害点密度 Wi 海拔 1 1/7 1/2 1/3 1/6 1/4 1/5 1/2 1/8 0.024 坡度 1 5 3 1/3 1 1 2 1/4 0.115 坡向 1 1 1/6 1/2 1/3 1/2 1/8 0.036 归一化植被覆盖度 1 1/5 1 1/2 1 1/6 0.052 工程地质岩组 1 4 2 3 1/3 0.202 道路 1 1/2 1 1/5 0.068 断层 1 2 1/4 0.106 水系 1 1/5 0.058 灾害点密度 1 0.339
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表 3 不同模型AUC值
Table 3. AUC values of different models
剔除因子 未剔除 海拔 海拔、NDVI 海拔、NDVI、水系 AUC值 0.890 0.875 0.871 0.871
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表 4 评价因子确定性系数值及权重值
Table 4. Coefficient of determination values and weight values for evaluation factors
评价因子 状态分级 $ CF $ $ {W}_{i-{\mathrm{AHP}}} $ $ {W}_{i-{\mathrm{IOE}}} $ $ {W}_{i-{\mathrm{AHP -IOE}}} $ 评价因子 状态分级 $ CF $ $ {W}_{i-{\mathrm{AHP}}} $ $ {W}_{i-{\mathrm{IOE}}} $ $ {W}_{i-{\mathrm{AHP -IOE}}} $ 海拔/m 1582 ～1724 −0.357 0.024 0.116 0.067 0.8～1 −0.827 1724 ～1960 0.658 工程地质岩组 松散土体 −0.282 0.202 0.042 0.126 1 960～ 2167 0.280 碎屑岩岩组 −0.260 2167 ～2394 −0.463 碳酸盐岩岩组 0.122 2394 ～2644 −1.000 碳酸盐岩夹碎屑岩组 0.605 2644 ～3098 −1.000 变质岩岩组 −0.127 坡度/（°） 0～8 −0.765 0.115 0.026 0.073 岩浆岩岩组 0.687 8～16 0.374 距道路距离/m 0～300 0.601 0.068 0.035 0.052 16～24 0.336 300～600 0.453 24～32 0.273 600～900 −0.215 32～40 0.007 900～ 1200 0.115 >40 0.332 > 1200 −0.443 坡向 平面 −1.000 0.036 0.031 0.034 距断层距离/m 0～300 0.437 0.106 0.021 0.066 北 −0.420 300～600 0.471 东北 −0.228 600～900 0.451 东 0.149 900～ 1200 0.299 东南 −0.448 > 1200 −0.439 南 0.152 距水系距离/m 0～200 0.651 0.058 0.066 0.062 西南 0.350 200～400 0.144 西 0.337 400～600 −0.179 西北 −0.096 600～800 −0.678 归一化植被
覆盖度0～0.2 −1.000 0.052 0.092 0.071 >800 −0.634 0.2～0.4 −0.312 灾害点密度/km2 0 −1.000 0.339 0.572 0.449 0.4～0.6 0.394 0～1 0.808 0.6～0.8 0.386 1～2.11 0.958
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表 5 CF、CF-AHP、CF-IOE、CF-AHP-IOE模型分级结果
Table 5. Grading results of CF, CF-AHP 、CF-IOE、CF-AHP-IOE models
评价模型 易发分区 分级面积/km2 面积占比/% 灾害频数 灾害占比/% 灾害密度/km2 频率比值 频率比值占比/% CF 低 241.328 31.6 1 1.0 0.004 0.032 0.53 中 208.166 27.2 5 5.1 0.024 0.186 3.10 高 198.697 26.0 14 14.1 0.070 0.544 9.07 极高 116.628 15.2 79 79.8 0.677 5.233 87.29 CF-AHP 低 365.938 47.8 0 0 0 0 0 中 197.212 25.8 1 1.0 0.005 0.039 0.50 高 107.775 14.1 22 22.2 0.204 1.577 20.04 极高 93.894 12.3 76 76.8 0.809 6.253 79.46 CF-IOE 低 371.350 48.6 0 0 0 0 0 中 201.359 26.3 1 1.0 0.005 0.038 0.51 高 86.153 11.3 15 15.2 0.174 1.345 18.09 极高 105.957 13.9 83 83.8 0.783 6.052 81.40 CF-AHP-IOE 低 300.304 39.3 0 0 0 0 0 中 202.226 26.4 0 0 0 0 0 高 130.706 17.1 9 9.1 0.069 0.532 9.15 极高 131.584 17.2 90 90.9 0.684 5.284 90.85
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