PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (11): 1380-1390.doi: 10.18306/dlkxjz.2017.11.007
• Orginal Article • Previous Articles Next Articles
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:
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.
Tab.1
Evaluation index system and measurement method of social vulnerability to floods"
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 |
Tab.4
Assessment results of social vulnerability to floods in the case study villages"
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 |
Tab.5
Distribution of rural households of different social vulnerability grades"
村庄名称 | 高脆弱度 | 中脆弱度 | 低脆弱度 | 行政村合计 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
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 |
Tab.6
Error result of training and test for BP neural network model"
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 |
[1] | 程先富, 郝丹丹. 2015. 基于OWA-GIS的巢湖流域洪涝灾害风险评价[J]. 地理科学, 35(10): 1312-1317. |
[Cheng X F, Hao D D.2015. Flood risk assessment in Chaohu Basin based on OWA-GIS[J]. Scientia Geographica Sinica, 35(10): 1312-1317.] | |
[2] |
方创琳, 王岩. 2015. 中国城市脆弱性的综合测度与空间分异特征[J]. 地理学报, 70(2): 234-247.
doi: 10.11821/dlxb201502005 |
[Fang C L, Wang Y.2015. A comprehensive assessment of urban vulnerability and its spatial differentiation in China[J]. Acta Geographica Sinica, 70(2): 234-247.]
doi: 10.11821/dlxb201502005 |
|
[3] | 封铁英, 王毅敏, 段兴民. 2005. 企业资本结构及其影响因素的关系研究: 多元线性回归模型与神经网络模型的比较与应用[J]. 系统工程, 23(1): 42-48. |
[Feng T Y, Wang Y M, Duan X M.2005. A study on the relationship between capital structure of enterprise and its determinants: Comparison and application of multivariate linear regression model and neural network model[J]. Systems Engineering, 23(1): 42-48.] | |
[4] | 何艳冰, 黄晓军, 翟令鑫, 等. 2016. 西安快速城市化边缘区社会脆弱性评价与影响因素[J]. 地理学报, 71(8): 1315-1328. |
[He Y B, Huang X J, Zhai L X, et al.2016. Assessment and influencing factors of social vulnerability to rapid urbanization in urban fringe: A case study of Xi'an[J]. Acta Geographica Sinica, 71(8): 1315-1328.] | |
[5] | 胡泽文, 武夷山. 2012. 科技产出影响因素分析与预测研究: 基于多元回归和BP神经网络的途径[J]. 科学学研究, 30(7): 992-1004. |
[Hu Z W, Wu Y S.2012. Research on analysis of influencing factors and prediction for scientific and technological outputs: An approach based on multiple linear regression and BP neural network[J]. Studies in Science of Science, 30(7): 992-1004.] | |
[6] |
黄晓军, 黄馨, 崔彩兰, 等. 2014. 社会脆弱性概念、分析框架与评价方法[J]. 地理科学进展, 33(11):1512-1525.
doi: 10.11820/dlkxjz.2014.11.008 |
[Huang X J, Huang X, Cui C L, et al.2014. The concept, analytical framework and assessment method of social vulnerability[J]. Progress in Geography, 33(11): 1512-1525.]
doi: 10.11820/dlkxjz.2014.11.008 |
|
[7] | 蒋卫国, 盛绍学, 朱晓华, 等. 2008. 区域洪水灾害风险格局演变分析: 以马来西亚吉兰丹州为例[J]. 地理研究, 27(3): 502-508, 727. |
[Jiang W G, Sheng S X, Zhu X H, et al.2008. Change and spatial pattern of flood disaster risk[J]. Geographical Research, 27(3): 502-508, 727.] | |
[8] | 黎洁, 李亚莉, 邰秀军, 等. 2009. 可持续生计分析框架下西部贫困退耕山区农户生计状况分析[J]. 中国农村观察, (5): 29-38. |
[Li J, Li Y L, Tai X J, et al.2009. On the rural households livelihood in the western poor areas after the slopping land conversion program within the sustainable livelihood analysis framework from the rural households survey in the Zhouzhi County, Shanxi Province[J]. China Rural Survey, (5): 29-38.] | |
[9] |
李博, 杨智, 苏飞. 2015. 基于集对分析的大连市人海经济系统脆弱性测度[J]. 地理研究, 34(5): 967-976.
doi: 10.11821/dlyj201505015 |
[Li B, Yang Z, Su F.2015. Measurement of vulnerability in human-sea economic system based on set pair analysis: A case study of Dalian City[J]. Geographical Research, 34(5): 967-976.]
doi: 10.11821/dlyj201505015 |
|
[10] | 廖翼, 周发明. 2012. 中国烟草及烟草制品的国际竞争力及影响因素分析[J]. 国际贸易问题, (3): 22-33. |
[Liao Y, Zhou F M.2012. Analysis on international competitiveness of Chinese tobacco and tobacco products and its influencing factors[J]. Journal of International Trade, (3): 22-23.] | |
[11] |
刘家福, 张柏. 2015. 暴雨洪灾风险评估研究进展[J]. 地理科学, 35(3): 346-351.
doi: 10.11820/dlkxjz.1997.03.005 |
[Liu J F, Zhang B.2015. Progress of rainstorm flood risk assessment[J]. Scientia Geographica Sinica, 35(3): 346-351.]
doi: 10.11820/dlkxjz.1997.03.005 |
|
[12] |
刘柯. 2007. 基于主成分分析的BP神经网络在城市建成区面积预测中的应用: 以北京市为例[J]. 地理科学进展, 26(6): 129-137.
doi: 10.3969/j.issn.1007-6301.2007.06.014 |
[Liu K.2007. Application of BP neural network in the prediction of urban built- up area: A case study of Beijing[J]. Progress in Geography, 26(6): 129-137.]
doi: 10.3969/j.issn.1007-6301.2007.06.014 |
|
[13] |
刘凯, 任建兰, 程钰, 等. 2016. 黄河三角洲地区社会脆弱性评价与影响因素[J]. 经济地理, 36(7): 45-52.
doi: 10.15957/j.cnki.jjdl.2016.07.006 |
[Liu K, Ren J L, Cheng Y, et al.2016. Yellow River Delta social vulnerability evaluation and influence factor[J]. Economic Geography, 36(7): 45-52.]
doi: 10.15957/j.cnki.jjdl.2016.07.006 |
|
[14] |
卢阳旭. 2013. 国外灾害社会学中的城市社区应灾能力研究: 基于社会脆弱性视角[J]. 城市发展研究, 20(9): 83-87, 118.
doi: 10.3969/j.issn.1006-3862.2013.09.015 |
[Lu Y X.2013. A review on city community disaster response capability in the sociology of disaster: Based on the perspective of social vulnerability[J]. Urban Development Studies, 20(9): 83-87, 118.]
doi: 10.3969/j.issn.1006-3862.2013.09.015 |
|
[15] | 秦大河, 张建云, 闪淳昌, 等. 2015. 中国极端天气气候事件和灾害风险管理与适应国家评估报告[M]. 北京: 科学出版社. |
[Qin D H, Zhang J Y, Shan C C, et al.2015. Zhongguo jiduan tianqi qihou shijian he zaihai fengxian guanli yu shiying guojia pinggu baogao[M]. Beijing, China: Science Press.] | |
[16] | 唐林楠, 刘玉, 潘瑜春, 等. 2016. 基于BP模型和Ward法的北京市平谷区乡村地域功能评价与分区[J]. 地理科学, 36(10): 1514-1521. |
[Tang L N, Liu Y, Pan Y C, et al.2016. Evaluation and zoning of rural regional multifunction based on BP model and ward method: A case in the Pinggu District of Beijing City[J]. Scientia Geographica Sinica, 36(10): 1514-1521.] | |
[17] | 王富喜, 毛爱华, 李赫龙, 等. 2013. 基于熵值法的山东省城镇化质量测度及空间差异分析[J]. 地理科学, 33(11): 1323-1329. |
[Wang F X, Mao A H, Li H L, et al.2013. Quality measurement and regional difference of urbanization in Shandong Province based on the entropy method[J]. Scientia Geographica Sinica, 33(11): 1323-1329.] | |
[18] |
王岩, 方创琳, 张蔷. 2013. 城市脆弱性研究评述与展望[J]. 地理科学进展, 32(5): 755-768.
doi: 10.11820/dlkxjz.2013.05.007 |
[Wang Y, Fang C L, Zhang Q.2013. Progress and prospect of urban vulnerability[J]. Progress in Geography. 32(5): 755-768.]
doi: 10.11820/dlkxjz.2013.05.007 |
|
[19] |
谢家智, 车四方, 林涌. 2017. 基于随机权神经网络的地震灾害经济损失评估与预测[J]. 灾害学, 32(1): 1-4, 10.
doi: 10.3969/j.issn.1000-811X.2017.01.001 |
[Xie J Z, Che S F, Lin Y.2017. Earthquake disaster economic loss estimation and prediction based on neural networks with random weights[J]. Journal of Catastrophology, 32(1): 1-4, 10.]
doi: 10.3969/j.issn.1000-811X.2017.01.001 |
|
[20] |
谢盼, 王仰麟, 刘焱序, 等. 2015. 基于社会脆弱性的中国高温灾害人群健康风险评价[J]. 地理学报, 70(7): 1041-1051.
doi: 10.11821/dlxb201507002 |
[Xie P, Wang Y L, Liu Y X, et al.2015. Incorporating social vulnerability to assess population health risk due to heat stress in China[J]. Acta Geographica Sinica, 70(7): 1041-1051.]
doi: 10.11821/dlxb201507002 |
|
[21] |
杨佩国, 靳京, 赵东升, 等. 2016. 基于历史暴雨洪涝灾情数据的城市脆弱性定量研究: 以北京市为例[J]. 地理科学, 36(5): 733-741.
doi: 10.13249/j.cnki.sgs.2016.05.011 |
[Yang P G, Jin J, Zhao D S, et al.2016. An urban vulnerability study based on historical flood data: A case study of Beijing[J]. Scientia Geographica Sinica, 36(5): 733-741.]
doi: 10.13249/j.cnki.sgs.2016.05.011 |
|
[22] |
尹卫霞, 余瀚, 崔淑娟, 等. 2016. 暴雨洪水灾害人口损失评估方法研究进展[J]. 地理科学进展, 35(2): 148-158.
doi: 10.18306/dlkxjz.2016.02.002 |
[Yin W X, Yu H, Cui S J, et al.2016. Review on methods for estimating the loss of life induced by heavy rain and floods[J]. Progress in Geography, 35(2): 148-158.]
doi: 10.18306/dlkxjz.2016.02.002 |
|
[23] | 游温娇, 张永领. 2013. 洪灾社会脆弱性指标体系研究[J]. 灾害学, 28(3): 215-220. |
[You W J, Zhang Y L.2013. Research on index system of social vulnerability for flood hazard[J]. Journal of Catastrophology, 28(3): 215-220.] | |
[24] | 赵庆良, 王军, 许世远, 等. 2010. 沿海城市社区暴雨洪水风险评价: 以温州龙湾区为例[J]. 地理研究, 29(4): 665-674. |
[Zhao Q L, Wang J, Xu S Y, et al.2010. Flood risk assessment of coastal community: A case study in Longwan District of Wenzhou City[J]. Geographical Research, 29(4): 665-674.] | |
[25] |
周扬, 李宁, 吴文祥. 2014. 自然灾害社会脆弱性研究进展[J]. 灾害学, 29(2): 128-135.
doi: 10.3969/j.issn.1000-811X.2014.02.025 |
[Zhou Y, Li N, Wu W X.2014. Research progress on social vulnerability to natural disasters[J]. Journal of Catastrophology, 29(2): 128-135.]
doi: 10.3969/j.issn.1000-811X.2014.02.025 |
|
[26] | Anderson M B.1995. Vulnerability to disaster and sustainable development: A general framework for assessing vulnerability[M]//Munasinghe M, Clarke C L. Disaster prevention for sustainable development: Economic and policy issues. Washington, DC: World Bank: 41-55. |
[27] |
Antwi-Agyei P, Dougill A J, Fraser E D G, et al.2013. Characterising the nature of household vulnerability to climate variability: Empirical evidence from two regions of Ghana[J]. Environment, Development and Sustainability, 15(4): 903-926.
doi: 10.1007/s10668-012-9418-9 |
[28] |
Bjarnadottir S, Li Y, Stewart M G.2011. Social vulnerability index for coastal communities at risk to hurricane hazard and a changing climate[J]. Natural Hazards, 59(2): 1055-1075.
doi: 10.1007/s11069-011-9817-5 |
[29] |
Cutter S L.2003. The vulnerability of science and the science of vulnerability[J]. Annals of the Association of American Geographers, 93(1): 1-12.
doi: 10.1111/1467-8306.93101 |
[30] |
Cutter S L, Finch C.2008. Temporal and spatial changes in social vulnerability to natural hazards[J]. Proceedings of the National Academy of Sciences of the United States of America, 105(7): 2301-2306.
doi: 10.1073/pnas.0710375105 pmid: 18268336 |
[31] |
Depietri Y, Welle T, Renaud F G.2013. Social vulnerability assessment of the Cologne urban area (Germany) to heat waves: Links to ecosystem services[J]. International Journal of Disaster Risk Reduction, 6: 98-117.
doi: 10.1016/j.ijdrr.2013.10.001 |
[32] | Dwyer A, Zoppou C, Nielsen O M, et al.2004. Quantifying social vulnerability: A methodology for identifying those at risk to natural hazards[R]. Canberra, Australia: Geoscience Australia. |
[33] |
Ebert A, Kerle N, Stein A.2009. Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and space borne imagery and GIS data[J]. Natural Hazards, 48(2): 275-294.
doi: 10.1007/s11069-008-9264-0 |
[34] | EM-DATA. 2016. Natural disasters profiles[EB/OL]. 2016-12-19[2017-04-09]. . |
[35] | Fekete A.2010. Assessment of social vulnerability for river-floods in Germany[D]. Bonn, Germany: University of Bonn. |
[36] |
Füssel H M, Klein R J T.2006. Climate change vulnerability assessments: An evolution of conceptual thinking[J]. Climatic Change, 75(3): 301-329.
doi: 10.1007/s10584-006-0329-3 |
[37] |
Holand I S, Lujala P, Rød J K.2011. Social vulnerability assessment for Norway: A quantitative approach[J]. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 65(1): 1-17.
doi: 10.1080/00291951.2010.550167 |
[38] | IPCC. 2013. Climate change 2013: The physical science basis: The working group I contribution to the IPCC fifth assessment report[M]. Cambridge, UK: Cambridge University Press. |
[39] |
Janssen M A, Schoon M L, Ke W M, et al.2006. Scholarly networks on resilience, vulnerability and adaptation within the human dimensions of global environmental change[J]. Global Environmental Change, 16(3): 240-252.
doi: 10.1016/j.gloenvcha.2006.04.001 |
[40] |
Li Z Q, Niu F, Fan J W, et al.2011. Long-term impacts of aerosols on the vertical development of clouds and precipitation[J]. Nature Geoscience, 4(12): 888-894.
doi: 10.1038/ngeo1313 |
[41] |
Liao K H.2012. A theory on urban resilience to floods: A basis for alternative planning practice[J]. Ecology and Society, 17(4): 48.
doi: 10.5751/ES-05231-170448 |
[42] |
Pandey R, Jha S.2012. Climate vulnerability index-measure of climate change vulnerability to communities: A case of rural Lower Himalaya, India[J]. Mitigation and Adaptation Strategies for Global Change, 17(5): 487-506.
doi: 10.1007/s11027-011-9338-2 |
[43] | Srivastava S, Velasquez J, Sirimanne S, et al.2012. Reducing vulnerability and exposure to disasters: Asia-Pacific disaster report 2012[R]. Bangkok, Thailand: ESCAP and UNISDR. |
|