地理科学进展 ›› 2017, Vol. 36 ›› Issue (11): 1380-1390.doi: 10.18306/dlkxjz.2017.11.007
出版日期:
2017-12-07
发布日期:
2017-12-07
通讯作者:
李同昇
E-mail:shyusherry@163.com;leetang@nwu.edu.cn
作者简介:
作者简介:石钰(1992-),女,湖北黄冈人,硕士研究生,主要从事城市与区域规划研究,E-mail:
基金资助:
Yu SHI(), Enpu MA, Tongsheng LI*(
), Yang RUI
Online:
2017-12-07
Published:
2017-12-07
Contact:
Tongsheng LI
E-mail:shyusherry@163.com;leetang@nwu.edu.cn
Supported by:
摘要:
降低洪灾社会脆弱度是缓解洪灾社会影响,建立洪水韧性的重要途径。本文从敏感性、适应性和暴露度3个方面构建微观尺度下的洪灾社会脆弱度评价指标体系,以安康市4个滨河村庄为例,运用基于熵权的综合指数法评价农户的洪灾社会脆弱度,并通过BP神经网络分析厘清评价指标与社会脆弱度之间的重要性关系,识别出洪灾社会脆弱度的主要影响因素。据此提出相应的对策建议作为降低农户洪灾社会脆弱度的实践依据。研究表明:①案例村调研样本中近一半的农户处于高社会脆弱度等级,由此推算,研究区有715个农户具有较高的洪灾社会脆弱度;②受访者健康状况、防汛信息渠道、避灾疏散方式、建筑质量、是否有病残人口、家庭收入多样性、5岁以下幼儿比重和60岁以上老年人比重是农户社会脆弱度的主要影响因素;③基于农户视角的洪灾社会脆弱度评价能准确地识别出脆弱度较高的农户,其结果在降低洪灾社会脆弱度方面更具有现实意义。
石钰, 马恩朴, 李同昇, 芮旸. 基于农户视角的洪灾社会脆弱度及影响因素——以安康市4个滨河村庄为例[J]. 地理科学进展, 2017, 36(11): 1380-1390.
Yu SHI, Enpu MA, Tongsheng LI, Yang RUI. Social vulnerable degree of floods and its influencing factors based on the perspective of rural households: A case study of four riverside villages in Ankang City[J]. PROGRESS IN GEOGRAPHY, 2017, 36(11): 1380-1390.
表1
洪灾社会脆弱度评价指标体系及测量方法"
目标层 | 准则层 | 指标层 | 指标向性 | 测量方法 |
---|---|---|---|---|
洪灾社会 脆弱度 | 敏感性 | x1:5岁以下幼儿比重 | 值正向 | 5岁以下幼儿人口/家庭总人口 |
x2:60岁以上老年人比重 | 值正向 | 60岁以上老年人口/家庭总人口 | ||
x3:受访者健康状况 | 代码值正向 | 良好=1,一般=2,较差=3,不好=4 | ||
x4:是否有病残人口 | 代码值负向 | 是=1,否=2 | ||
x5:建筑质量 | 代码值正向 | 好=1,较好=2,一般=3,较差=4,极差=5 | ||
x6:是否有易损商品 | 代码值负向 | 是=1,否=2 | ||
适应性 | x7:是否有稳定工作 | 代码值负向 | 是=1,否=2 | |
x8:家庭收入多样性 | 值正向 | |||
x9:防汛信息渠道 | 代码值负向 | 移动智能设备=1,PC互联网设备=2,电视新闻报道=3,社区广播和邻里交流=4,自行观察=5 | ||
x10:避灾疏散方式 | 代码值负向 | 私人机动车辆=1,城市公共交通=2,非机动车辆=3,步行=4 | ||
暴露度 | x11:工作地点是否在洪泛区内 | 代码值负向 | 是=1,否=2 | |
x12:洪泛区内的耕地比重 | 值正向 | 洪泛区内耕地面积/家庭总耕地面积 | ||
x13:房屋高程 | 代码值正向 | >258.93=1,258.93~256.55=2,256.55~251.86=3,251.86~247.61=4,<247.61=5,高程单位: m | ||
x14:房屋距河口距离 | 值负向 | 房屋到干支流交汇点的直线距离,单位: m |
表4
案例村洪灾社会脆弱度评价结果"
评价等级 | 评价值区间 | 户数/户 | 百分比/% | |
---|---|---|---|---|
脆弱度 | 高 | 0.1322~0.3529 | 120 | 47.06 |
中 | -0.0372~0.1321 | 88 | 34.51 | |
低 | -0.3931~-0.0371 | 47 | 18.43 | |
敏感性 | 高 | 0.2884~0.6513 | 51 | 20.00 |
中 | 0.1293~0.2883 | 107 | 41.96 | |
低 | 0.0000~0.1292 | 97 | 38.04 | |
适应性 | 高 | 0.4388~0.9444 | 33 | 12.94 |
中 | 0.1632~0.4387 | 83 | 32.55 | |
低 | 0.0000~0.1631 | 139 | 54.51 | |
暴露度 | 高 | 1.3860~0.8145 | 87 | 34.12 |
中 | 0.7665~1.3859 | 113 | 44.31 | |
低 | 0.1054~0.7664 | 55 | 21.57 |
表6
BP神经网络模型训练及检验误差结果"
隐含层神经元节点数 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|
训练 | 平方和误差 | 14.213 | 6.870 | 8.420 | 6.610 | 7.186 | 4.277 | 0.281 | 5.441 | 2.768 | 8.164 |
相对误差 | 0.154 | 0.830 | 0.093 | 0.076 | 0.078 | 0.055 | 0.003 | 0.060 | 0.032 | 0.093 | |
检验 | 平方和误差 | 5.320 | 6.708 | 2.848 | 5.351 | 5.030 | 4.659 | 1.236 | 3.516 | 3.562 | 4.372 |
相对误差 | 0.128 | 0.155 | 0.096 | 0.112 | 0.124 | 0.108 | 0.030 | 0.103 | 0.126 | 0.126 |
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