Evaluation of Landslide Susceptibility in Chinese Loess Plateau Based on IV-RF and IV-CNN Coupling Models
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摘要: 黄土高原在地质环境与人类活动的复杂互馈作用下易导致黄土崩滑灾害频发，亟需选择适用性的影响因子和训练模型开展滑坡易发性评价研究.本研究以黄土高原为研究区，基于野外滑坡调查和资料收集，构建涵盖地形地貌、基础地质环境、气象水文、人类活动、土壤物理化学性质以及植被覆盖的评价体系，采用信息量模型(IV)分别联接到随机森林模型(RF)和卷积神经网络模型(CNN)构建耦合模型IV-RF和IV-CNN，开展滑坡易发性评价研究.结果表明，耦合模型(IV-RF、IV-CNN)的精度均高于独立模型(RF、CNN)，4种模型的AUC值分别为0.916、0.938、0.878、0.853，IV-CNN具有更强的预测能力和精度.IV-CNN模型的极高、高、中、低、极低易发性区域面积占比分别为8.78%、7.47%、15.34%、19.82%、47.87%，主要分布在黄土高原南部和东部地质环境复杂和人类活动强烈的山地、黄土梁峁地区.坡度、侵蚀类型、地貌类型、粘粒含量、距道路距离在贡献率分析中排在前5位，是影响滑坡发育的主控因子.本研究旨在为黄土高原滑坡灾害的预测和防治工作提供可靠的科学依据，为滑坡易发性评价研究深化建模思路，优化独立模型评价结果不确定性问题.Abstract: Due to the complex interaction between geological environment and human activities, the Chinese Loess Plateau (CLP) is prone to frequent landslides. It is urgent to carry out landslide vulnerability assessment, selecting suitable influencing factors and training models. In this study, the CLP was taken as the study area. Based on field landslide survey and data collection, an evaluation system including topography, basic geological environment, meteorology and hydrology, human activities, soil physical and chemical properties, and vegetation coverage was built. The information model (IV) was used to connect the random forest model (RF) and convolutional neural network model (CNN) to build coupling models IV-RF and IV-CNN, and landslide susceptibility evaluation research was carried out. The results show that the accuracy of the coupling model (IV-RF, IV-CNN) is higher than that of the independent model (RF, CNN), and the AUC values of the four models are 0.916, 0.938, 0.878, and 0.853, respectively. The IV-CNN has stronger prediction ability and accuracy. The areas of extremely high, high, medium, low, and extremely low vulnerability areas in the IV-CNN model account for 8.78%, 7.47%, 15.34%, 19.82%, and 47.87% respectively, which are mainly distributed in the mountainous and loess hilly areas with complex geological environment and strong human activities in the south and east of the loess plateau. Slope, erosion type, landform type, clay content and distance from the road rank in the top five in the contribution rate analysis, and are the main control factors affecting the landslide development. The purpose of this study is to provide reliable scientific basis for the prediction and prevention of landslide disasters in the CLP, deepen the modeling idea for landslide vulnerability evaluation research, and optimize the uncertainty of independent model evaluation results.
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图 1 黄土高原滑坡分布及典型案例
Fig. 1. Landslide distribution and typical cases in the Chinese loess plateau
图 2 黄土高原滑坡核密度估计
Fig. 2. Kernel density estimation of landslides in Chinese loess plateau
图 3 基于IV-RF模型和IV-CNN模型的滑坡易发性评价技术路线
Fig. 3. Technical route for landslide sensitivity evaluation based on IV-RF model and IV-CNN model
图 5 评价因子分级信息量值与滑坡分布百分比
图 5h：TPI值表示滑坡位置的含义，处于≤-1区间为山谷；处于-1~-0.5区间为高坡；处于-0.5~0.5区间为平地；处于0.5~1区间为低坡；处于≥1区间为山脊；图 5k：1.山地；2.黄土梁峁；3.台塬；4.塬；5.冈蚀地貌；6.台地；7.平原；8.冲积扇平原；9.低河漫滩；图 5l：11.水力侵蚀微度；12.水力侵蚀轻度；13.水力侵蚀中度；14.水力侵蚀强度；15.水力侵蚀极强度；16.水力侵蚀剧烈；21.风力侵蚀微度；22.风力侵蚀轻度；23.风力侵蚀中度；24.风力侵蚀强度；25.风力侵蚀极强度；26.风力侵蚀剧烈；31.冻融侵蚀微度；32.冻融侵蚀轻度；33.冻融侵蚀中度；34.冻融侵蚀强度
Fig. 5. Grading information value of evaluation factors and percentage of landslide distribution
图 7 黄土高原滑坡易发性分区制图
Fig. 7. Mapping of landslide sensitivity zoning in the Chinese Loess Plateau
表 1 黄土高原滑坡灾害调查基础信息
Table 1. Results of precision evaluation indexes of different models
甘肃 河南 内蒙古 宁夏 青海 山西 陕西 滑坡数量(个) 1 643 773 41 328 511 1 255 1 996 威胁人口(人) 201 189 8 417 9 366 14 321 49 809 43 726 113 299 威胁财产(万元) 216 802.9 37 430.16 19 781.8 25 335.5 38 289.9 67 611.1 141 526.9 
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表 2 研究区所有数据来源与因子评价体系
Table 2. All data sources and factor evaluation system in the study area
一级因子 二级因子 数据来源 数据类型 精度 地形地貌 高程 ASTER卫星 连续型 30 m 坡度 基于SAGA 7.0软件地形分析模块和ASTER 30 m-DEM计算和提取 连续型 30 m 坡向 连续型 30 m 曲率 连续型 30 m 地形粗糙度 连续型 30 m 地形起伏度 连续型 30 m 地表切割度 连续型 30 m 地形位置指数(TPI) 连续型 30 m 地貌类型 中华人民共和国地貌图集 离散型 30 m 基础地质 距断层距离 中国国家地质资料数据中心 连续型 500 m 风化层厚度 美国NASA(DACC)部门 连续型 1 km 侵蚀类型 中华人民共和国行业标准SL190-96《土壤侵蚀分类分级标准》 离散型 1 km PGA 中国地震动峰值加速度区划图GB18306-2015 离散型 500 m 气象水文 距河流距离 中国1:400万主要基础数据集 连续型 500 m 地形湿度指数(TWI) 基于SAGA 7.0软件地形分析模块和ASTER 30 m-DEM计算和提取 连续型 30 m 径流强度指数(SPI) 连续型 30 m 温差 中国气象数据网 连续型 1 km 降水量 中国气象数据网 连续型 1 km 土壤物理化学性质 粘粒含量 《Predictive Soil Mapping with R》 连续型 1 km 土壤容重 《Predictive Soil Mapping with R》 连续型 1 km pH值 《Predictive Soil Mapping with R》 连续型 1 km 人类活动 距道路距离 中国1:400万主要基础数据集 连续型 500 m 人口密度 美国NASA(SEDAC)部门 连续型 1 km 植被覆盖 NDVI 地理空间数据云Landsat-8遥感影像 连续型 1 km
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表 3 研究区影响因子的方差膨胀系数和容忍度
Table 3. Variance expansion coefficient and tolerance of influencing factors in the study area
影响因子 TOL VIF 影响因子 TOL VIF 高程 0.383 2.614 风化层厚度 0.942 1.061 坡度 0.156 6.430 PGA 0.598 1.671 坡向 0.983 1.017. 温差 0.332 3.014 曲率 0.437 2.286 NDVI 0.347 2.881 地表粗糙度 0.137 7.471 距断层距离 0.518 1.932 地表起伏度 0.142 7.045 距河流距离 0.841 1.190 地形切割度 0.133 7.541 距道路距离 0.908 1.101 TPI 0.330 3.030 降水量 0.209 4.776 TWI 0.644 1.552 人口密度 0.858 1.166 SPI 0.973 1.028 黏粒含量 0.614 1.628 地貌类型 0.777 1.287 容重 0.567 1.765 侵蚀类型 0.690 1.449 pH值 0.788 1.269
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表 4 不同模型滑坡易发性分区结果
Table 4. Landslide susceptibility zoning results of different models
模型 易发性
分区分区面积(km2) 分区面积占比(%) 滑坡数量
(个)滑坡数量占比(%) 滑坡密度
(个/km2)极低易发区 276 947.05 37.83 419 6.43 0.001 5 低易发区 158 161.20 21.60 913 14.01 0.005 8 IV-RF 中易发区 152 122.02 20.78 1 332 20.44 0.008 8 高易发区 53 706.53 7.34 1 244 19.09 0.023 2 极高易发区 91 126.22 12.45 2 609 40.03 0.028 6 极低易发区 246 144.33 33.62 368 5.65 0.001 5 低易发区 172 161.04 23.52 928 14.24 0.005 4 RF 中易发区 165 922.21 22.67 1 401 21.50 0.008 4 高易发区 55 709.23 7.61 1 111 17.05 0.019 9 极高易发区 92 126.22 12.58 2 709 41.57 0.029 4 极低易发区 352 947.06 47.87 485 4.37 0.001 4 低易发区 146 161.28 19.82 902 13.84 0.006 7 IV-CNN 中易发区 113 121.52 15.34 1 053 16.16 0.012 5 高易发区 55 106.94 7.47 1 654 25.38 0.024 6 极高易发区 64 726.22 8.78 2 423 40.25 0.023 8 极低易发区 383 846.51 52.43 321 4.93 0.000 8 低易发区 157 377.92 21.50 974 14.95 0.006 2 CNN 中易发区 102 388.24 13.99 1 348 20.68 0.013 2 高易发区 46 492.08 6.35 1 886 28.94 0.040 6 极高易发区 41 958.27 5.73 1 988 30.50 0.047 4
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表 5 不同模型精度评价指标结果
Table 5. Results of precision evaluation indexes of different models
模型 模型精度评价指标 AUC 准确率 精确率 召回率 F1分数 IV-RF 0.916 0.823 0.901 0.904 0.882 RF 0.878 0.812 0.821 0.812 0.819 IV-CNN 0.938 0.842 0.926 0.934 0.912 CNN 0.853 0.816 0.857 0.843 0.841
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