Vulnerability assessment of landslide hazards based on hazard intensity at slope level: A case study in Xiangxiang county of Hunan
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摘要: 以斜坡为单元，基于潜在灾害强度的区域性易损性评价是地质灾害防治亟待解决的重要问题之一。以湖南省湘乡市为研究区，在采用加权信息量方法进行易发性区划的基础上，逐个提取斜坡单元最高易发值点的高程、坡高、坡度、坡向、月平均降雨量为特征参数，分别代入BP神经网络、PSO-BP神经网络、随机森林及支持向量机模型。通过训练与精度测试对比，构建基于PSO优化BP神经网络算法的滑坡体积预测模型，建立以灾害体积为灾害强度指标，以建筑密度、人口密度、财产密度等为脆弱性指标的易损性综合评价模型。针对研究区开展基于潜在灾害强度的区域性易损性评价，完成高易损区（面积占比1.5%）、中易损区（面积占比28.5%）和低易损区（面积占比70%）的区划，实现了区域性易损性评价过程中致灾体灾害强度与承灾体脆弱性的有机结合，增强了评价的客观性和科学性。
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关键词:
- 滑坡易损性评价 /
- 滑坡体积 /
- PSO-BP神经网络 /
- 斜坡单元
Abstract: Taking a slope as a unit, the regional vulnerability assessment based on potential disaster intensity is one of the important problems to be solved urgently. In this paper, the city of Xiangxiang in Hunan is selected as the research area. On the basis of susceptibility regionalization with the weighted information value method, the elevation, slope height, slope, slope direction and monthly average rainfall of the highest prone points of slope units are extract one by one as the characteristic parameters, which are put into the BP neural network, PSO-BP neural network, random forest and support vector machine model, respectively. A landslide volume prediction model based on BP neural network algorithm optimized by PSO is constructed through training and precision test comparison. A comprehensive vulnerability evaluation model is established with disaster volume as disaster intensity index and building density, population density and property density as vulnerability indexes. Regional vulnerability evaluation based on potential disaster intensity is carried out for the study area. The divisions of high-vulnerable areas (1.5% of the total area), medium-vulnerable areas (28.5% of the total area) and low-vulnerable areas (70% of the total area) are completed, which realize the organic combination of the disaster intensity of the disaster-causing body and the vulnerability of the disaster-bearing body in the process of regional vulnerability evaluation, and enhance the objectivity and scientific nature of the evaluation. -
图 1 斜坡单元灾害强度评价流程图
Figure 1. Flow chart of the slope unit disaster intensity assessment
图 2 湘乡市地质灾害易发性分区图
Figure 2. Zoning map of geological hazard susceptibility in the city of Xiangxiang
图 3 各模型测试集预测误差统计曲线
Figure 3. Statistical curves of prediction error for each model test set
图 4 湘乡市斜坡单元灾害强度分布图
Figure 4. Disaster intensity distribution map of slope units in the city of Xiangxiang
图 5 湘乡市斜坡单元脆弱性分布图
Figure 5. Vulnerability distribution map of slope units in the city of Xiangxiang
图 6 湘乡市斜坡单元易损性分区图
Figure 6. Vulnerability distribution map of slope units in the city of Xiangxiang
表 1 斜坡单元易损性综合评价
Table 1. Comprehensive evaluation of the vulnerability of slope units
易损性等级 脆弱性等级 低脆弱性 中脆弱性 高脆弱性 灾害强度等级 弱灾害强度 低 中 中 中灾害强度 低 中 高 强灾害强度 中 高 高 
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表 2 研究区基础数据
Table 2. Basic data of the study area
名称 类型 精度 遥感影像 栅格 0.5 m DEM 栅格 1∶10 000 工程地质图、土地利用类型图 矢量 1∶50 000 断层图、路网图 矢量 1∶50 000 行政区划图 矢量 1∶10 000 降雨数据 数据表 − 历史灾害点 数据表 − GDP 数据表 湘乡市 人口、建筑面积、道路、财产 数据表 斜坡单元 斜坡单元面积 数据表 斜坡单元
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表 3 易发性评价指标分区结果
Table 3. Partition results of the susceptibility evaluation indicators
评价指标 二级指标区间 高程/m 32~<101；101~<171；171~<267；267~<409；≥409 坡度/（°） 0~<6；6~<17；17~<28；28~<40；≥40 坡向 平面；北；东北；东；东南；南；西南；西；西北 工程地质岩组 硅质岩、硅质板岩；浅变质砂岩夹板岩；板岩；砂岩、砂砾岩；碳酸盐岩与碎屑岩互层；碳酸盐岩；岩浆岩；土体；红色碎屑岩；砂岩、页岩；硅质岩、硅质页岩 距断层距离/m <100；100~<200；200~<300；300~<400；≥400 距道路距离/m <100；100~<200；200~<300；300~<400；≥400 土地利用情况 耕地；林地；草地；水域；城乡、工矿居民用地；未利用土地类型 月平均降雨量/mm <100；100~<150；150~<200；≥200
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表 4 湘乡市地质灾害易发性分区结果
Table 4. Results of geological hazard susceptibility zoning in the city of Xiangxiang
易发性分区 面积比例/% 灾害点数量/个 灾害点比例/% 灾积比 高易发 8.2 216 71.3 1.43 中易发 39.3 75 24.8 0.10 低易发 52.5 12 3.9 0.012
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表 5 指标因子相关性分析
Table 5. Correlation of the controlling factors
指标因子 高程 坡高 坡度 坡向 工程地质岩组 距断层距离 距道路距离 土地利用情况 月平均降雨量 滑坡体积 高程 1 0.057 −0.055 −0.036 −0.169 −0.358 0.328 0.103 0.013 0.239 坡高 1 0.042 −0.028 −0.31 −0.26 −0.199 0.168 0.034 0.333 坡度 1 −0.027 −0.428 −0.104 0.245 0.051 −0.133 −0.205 坡向 1 0.003 0.007 −0.066 −0.493 −0.123 0.196 工程地质岩组 1 0.07 0.208 −0.024 0.043 0.003 距断层距离 1 −0.211 −0.102 0.207 −0.026 距道路距离 1 0.284 0.022 0.06 土地利用情况 1 −0.047 −0.102 月平均降雨量 1 −0.313 滑坡体积 1
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表 6 各模型预测结果精度对比
Table 6. Comparison of prediction accuracy of each model
测试集结果 预测正确样本量/个 预测错误样本量/个 预测精度/% BP神经网络 21 15 58.33 PSO-BP神经网络 29 7 80.56 随机森林 18 18 50.00 支持向量机 25 11 69.44
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表 7 斜坡单元灾害强度等级分区结果
Table 7. Results of disaster intensity classification of slope units
预测体积分区/m³ 灾害强度等级 <15 000 弱灾害强度 15 000～45 000 中灾害强度 >45 000 强灾害强度
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表 8 脆弱性评价指标组合权重结果
Table 8. Combined weight results of vulnerability assessment indicators
评价因子 权重值 人口密度 0.3072 建筑密度 0.2160 道路密度 0.2141 GDP密度 0.1607 财产密度 0.1026
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表 9 斜坡单元脆弱性等级分区结果
Table 9. Results of vulnerability classification of slope units
脆弱性值分区 脆弱性等级 ＜0.0845 低脆弱性 0.0845 ～ 0.1750 中脆弱性 ＞0.1750 高脆弱性
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表 10 斜坡单元易损性统计结果
Table 10. Statistical results of slope unit vulnerability
易损性分区 斜坡单元数量/个 斜坡单元数量占比/% 高易损区 124 2.6 中易损区 2 023 42.7 低易损区 2 587 54.7
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