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

doi:10.18306/dlkxjz.2017.11.007

摘要/Abstract

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。

关键词: 农户视角, 洪灾, 脆弱度评价, 影响因素, 安康市滨河村庄

Abstract:

Reducing social vulnerability to floods is an important way to alleviate the social impact of flood disasters and to improve flood resilience. This article constructs an evaluation index system for social vulnerability to floods with regard to sensitivity, adaptability, and exposure based on a micro-scale analysis. Taking four riverside villages in Ankang City as an example, we adopted the integrated index weighted by entropy method to evaluate rural households' social vulnerability to floods. Then we clarify the importance of the relationship between evaluation index and social vulnerability by BP neural network method, and identify the major influencing factors of flood vulnerability. Accordingly, countermeasures and suggestions are put forward as a practical basis for reducing the rural households' social vulnerability to floods. The main results of this research are as follows. (1) Half of the households (715) in the case study villages showed a high level of social vulnerability. (2) The difference in social vulnerability is mainly a result of eight factors, including health status of the respondents, information channel on flood prevention, evacuation method, building quality, sick or disabled person in a household, household income diversification, proportion of children under the age of 5, and proportion of older people over 60 years of age. (3) Social vulnerability evaluation based on the perspective of rural households can accurately identify vulnerable farmers and analyze the influencing factors, which have more realistic significance for reducing the social vulnerability to floods of rural households.

Key words: rural households' perspective, floods, vulnerability evaluation, influencing factors, riverside village in Ankang City

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.

图/表 10

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图2

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	$x 8 = - ∑ i = 1 n P i · ln P i, n$ 为收入来源数,根据调查结果,n的取值为1、2、3、4
		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1

表2

表3

表4

表5

表6

图3

图4

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解释变量	方差扩大因子(VIF)
5岁以下幼儿比重	1.068
60岁以上老年人比重	1.296
受访者健康状况	1.324
有无病残人口	1.176
是否有易损商品	1.088

	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

村庄名称	高脆弱度			中脆弱度		低脆弱度			行政村合计
村庄名称	户数/户		百分比/%		户数/户	百分比/%	户数/户	百分比/%	户数/户	百分比/%
心石村	27	33.75		27	33.75	26	32.50		80	100
油坊村	26	41.94		28	45.16	8	12.90		62	100
白庙村	50	64.10		21	26.92	7	8.98		78	100
高井村	17	48.57		12	34.29	6	17.14		35	100

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
训练	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
检验	相对误差	0.128	0.155	0.096	0.112	0.124	0.108	0.030	0.103	0.126	0.126