基于斜坡单元灾害强度的滑坡灾害易损性评价 以湖南省湘乡市为例

陈宾; 魏娜; 张联志; 李颖懿; 刘宁; 屈添强

doi:10.16031/j.cnki.issn.1003-8035.202211901

基于斜坡单元灾害强度的滑坡灾害易损性评价——以湖南省湘乡市为例

陈宾^{1, 2,},
魏娜¹,
张联志^3, ,,
李颖懿¹,
刘宁¹,
屈添强³

1.
湘潭大学土木工程学院，湖南湘潭　411105
2.
湘潭大学岩土力学与工程安全湖南省重点实验室，湖南湘潭　411105
3.
湖南省国土空间调查监测所，湖南长沙　410000

基金项目: 湖南省创新性省份建设专项（2019RS1059）；国家自然科学基金项目（51774131；41972282）

详细信息

作者简介:
陈宾（1977—），男，博士，副教授，主要从事地质灾害防治方面的研究工作。E-mail:403021235@qq.com

通讯作者:
张联志（1988—），男，硕士，工程师，主要从事水工环地质工作。E-mail：2149859375@qq.com

中图分类号: P642.22
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出版历程
- 收稿日期: 2022-11-08
- 修回日期: 2023-04-03
- 网络出版日期: 2023-06-08
- 刊出日期: 2024-04-24

Vulnerability assessment of landslide hazards based on hazard intensity at slope level: A case study in Xiangxiang County of Hunan

1.
School of Civil Engineering, Xiangtan University, Xiangtan, Hunan　411105, China
2.
Hunan Provincial Key Laboratory of Geomechanics and Engineering Safety, Xiangtan University, Xiangtan, Hunan　411105, China
3.
Hunan Provincial Territorial Space Survey and Monitoring Institute, Changsha, Hunan　410000, China

摘要

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。
- 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.
- landslide vulnerability evaluation /
- landslide volume /
- PSO-BP neural network /
- slope element

HTML全文

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

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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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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
易损性等级		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	1.430
中易发	39.3	75	24.8	0.100
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	−0.310	−0.260	−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	0.070	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	0.060
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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