城市排水管网脆弱性变权评估研究
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摘要
为使"海绵城市"与城市排水管网融合发展,协同作用缓解城市内涝。以天津市为例,从"暴露度-敏感性-适应能力"三个维度构建了城市排水管网脆弱性评价指标体系;采用惩罚型变权综合评价模型进行城市排水管网的脆弱性"诊断";利用MATLAB实现GM(1,1)模型对城市排水管网的脆弱性综合指数预测。研究结果表明:在2008-2017年期间,天津市城市排水管网脆弱性指数在0. 442～0. 652之间波动,总体呈上升走势。由预测结果可知,该趋势一直延伸至2018-2020年,脆弱度稳步于"轻度";通过变权评价结果与常权评价结果的对比可知惩罚型变权综合评价模型能够消除常权评价结果中的泡沫,提高评价结果的客观性。
In order to integrate the "sponge city"with the urban drainage network,synergy can alleviate urban guilt. The study takes Tianjin as an example to construct the vulnerability evaluation index system of urban drainage network from the three dimensions of "exposure-sensitivity-adaptive ability". The punitive variable weight comprehensive evaluation model was used to conduct the "diagnosis"of the vulnerability of urban drainage networks. The GM( 1,1) model in MATLAB was used to predict the vulnerability index of urban drainage network. The results indicated that the vulnerability index of urban drainage network in Tianjin fluctuated between 0. 442 and 0. 652,and the overall trend was upward during the period of 2008-2017. According to the forecast results,the trend extend to 2018-2020,and the vulnerability is steadily in the "light"range. Through comparison between the results of the variable power evaluation and the constant power evaluation,the comprehensive evaluation model of the punitive variable can eliminate the bubble in the constant power and improve the objectivity of the evaluation result.
In order to integrate the "sponge city"with the urban drainage network,synergy can alleviate urban guilt. The study takes Tianjin as an example to construct the vulnerability evaluation index system of urban drainage network from the three dimensions of "exposure-sensitivity-adaptive ability". The punitive variable weight comprehensive evaluation model was used to conduct the "diagnosis"of the vulnerability of urban drainage networks. The GM( 1,1) model in MATLAB was used to predict the vulnerability index of urban drainage network. The results indicated that the vulnerability index of urban drainage network in Tianjin fluctuated between 0. 442 and 0. 652,and the overall trend was upward during the period of 2008-2017. According to the forecast results,the trend extend to 2018-2020,and the vulnerability is steadily in the "light"range. Through comparison between the results of the variable power evaluation and the constant power evaluation,the comprehensive evaluation model of the punitive variable can eliminate the bubble in the constant power and improve the objectivity of the evaluation result.
引文
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[23]张路路,郑新奇,张春晓,等.基于变权模型的唐山城市脆弱性演变预警分析[J].自然资源学报,2016,31(11):1858-1870.
[24]LI Hongmin,WANG Jianzhou,LU Haiyan,et al. Research and application of a combined model based on variable weight for short term wind speed forecasting[J]. Renewable Energy,2018,116:669-684.
[25]WU Qiang,LI Bo,CHEN Yulong. Vulnerability assessment of groundwater inrush from underlying aquifers based on variable weight model and its application[J]. Water Resources Management,2016,30(10):3331-3345.
[26]WU Qiang,ZHAO Dekang,WANG Yang,et al. Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory[J]. Hydrogeology Journal,2017,25(10):1-15.
[27]付博,姜琦刚,任春颖.扎龙湿地生态脆弱性评价与分析[J].干旱区资源与环境,2011,25(1):49-52.
[28]孙平军,修春亮,王忠芝.基于PSE模型的矿业城市生态脆弱性的变化研究———以辽宁阜新为例[J].经济地理,2010,30(8):1354-1359.
[29]李永化,范强,王雪,等.基于SRP模型的自然灾害多发区生态脆弱性时空分异研究———以辽宁省朝阳县为例[J].地理科学,2015,35(11):1452-1459.
[30]胡雅杰,董阳,郭映,等.环江流域径流模拟研究[J].资源科学,2014,36(10):2038-2045.
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[32] POLSKY C,NEFF R,YARNAL B. Building comparable global change vulnerability assessments:the vulnerability scoping diagram[J]. Global Environmental Change,2007,17(3-4):472-485.
[33]SCHAUSER I,SCHNEIDERBAUER S,HARVEY A,et al.Urban regions:vulnerabilities,vulnerability assessments by indicators and adaptation options for climate change impacts,december[EB/OL].[2018-5-20]. http://www. mcrit. com/ADJUNTS/ciutats_sostenibles/UE_paper. pdf.
[34]SWART R,FONS J,GEERTSEMA W,et al. Urban vulnerability indicators a joint report of ETC-CCA and ETCSIA[J]. Social Indicators Research,2012,13(6):201-227.
[35]陈佳,杨新军,尹莎,等.基于VSD框架的半干旱地区社会———生态系统脆弱性演化与模拟[J].地理学报,2016,71(7):1172-1188.
[36]王思思,于迪,车伍.国际城市暴雨内涝脆弱性评估和适应性对策研究[J].城市发展研究,2015,22(7):23-26.
[37]吴冠岑,牛星.土地生态安全预警的惩罚型变权评价模型及应用———以淮安市为例[J].资源科学,2010,32(5):992-999.
[38]李德清,李洪兴.状态变权向量的性质与构造[J].北京师范大学学报(自然科学版),2002(4):455-461.
[39]SONG D,KEITH W H,DANG Y. Forecasting China's electricity consumption using a new grey prediction model[J]. Energy,2018,149:314-328.
[40] XU Ning,DANG Yaoguo,GONG Yande. Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China[J]. Energy,2017,118:473-380.
[41]韦师.基于灰色预测模型的我国就业发展趋势分析[J].统计与决策,2018,34(4):109-111.
[2]吴丹洁,詹圣泽,李友华,等.中国特色海绵城市的新兴趋势与实践研究[J].中国软科学,2016(1):79-97.
[3]程钰,任建兰,徐成龙.资源衰退型城市人地系统脆弱性评估———以山东枣庄市为例[J].经济地理,2015,35(3):87-93.
[4] SALAS J,YEPES V. Urban vulnerability assessment:advances from the strategic planning outlook[J]. Journal of Cleaner Production,2018,179:554-558.
[5]DUMITRA爦CU M,MOCANU I,MITRICB,et al. The assessment of socio-economic vulnerability to drought in Southern Romania(Oltenia Plain)[J]. International journal of disaster risk reduction,2018,27:142-154.
[6]袁海红,牛方曲,高晓路.城市经济脆弱性模拟评估系统的构建及其应用[J].地理学报,2015,70(2):271-282.
[7] LI He,JING Shen,YANG Zhang. Ecological vulnerability assessment for ecological conservation and environmental management[J]. Journal of Environmental Management,2018,206:1115-1125.
[8]徐君,李贵芳,王育红.国内外资源型城市脆弱性研究综述与展望[J].资源科学,2015,37(6):1266-1278.
[9]HUFSCHMIDT G. A comparative analysis of several vulnerability concepts[J]. Natural Hazards,2011,58(2):621-643.
[10] MCCARTHY J J,CANZIANI O F,LEARY N A,et al.IPCC climate change 2001:impacts,adaptation,and vulnerability[M]. Contribution of Working Group II to the Third Assessment Report. Cambridge University Press,2001:81-111.
[11]CHI Yuan,SHI Honghua,WANG Yuanyuan,et al. Evaluation on island ecological vulnerability and its spatial heterogeneity[J]. Marine Pollution Bulletin,2017,125(1-2):216-241.
[12] CHANG Lifang,HUANG Shuli. Assessing urban flooding vulnerability with an energy approach[J]. Landscape&Urban Planning,2015,143:11-24.
[13]方创琳,王岩.中国城市脆弱性的综合测度与空间分异特征[J].地理学报,2015,70(2):234-247.
[14]杨文健,李晓明.长江经济带城市综合脆弱性影响因素及其测度———以长江经济带的36个城市为例[J].城市问题,2017(1):19-28.
[15]RAUFIRAD V,HUNTER R,ENDRESS B A,et al. Application of vulnerability assessment to a grazed rangeland:toward an integrated conceptual framework[J]. Rangelands,2018,40(1):17-23.
[16]龚艳冰,戴靓靓,杨舒馨.云南省农业旱灾社会脆弱性评价研究[J].水资源与水工程学报,2017,28(6):239-243.
[17]安士伟,万三敏,李小建.城市脆弱性的评估与风险控制———以河南省为例[J].经济地理,2017,37(5):81-86.
[18]潘争伟,周戎星,王艳华.区域水资源系统脆弱性评价的集对分析与可变模糊集方法[J].中国人口·资源与环境,2016,26(S2):198-202.
[19]李博,杨智,苏飞.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,2015,34(5):967-976.
[20]彭飞,韩增林,杨俊,等.基于BP神经网络的中国沿海地区海洋经济系统脆弱性时空分异研究[J].资源科学,2015,37(12):2441-2450.
[21]杨新军,张慧,王子侨.基于情景分析的西北农村社会-生态系统脆弱性研究———以榆中县中连川乡为例[J].地理科学,2015,35(8):952-959.
[22]李彤玥.基于“暴露-敏感-适应”的城市脆弱性空间研究———以兰州市为例[J].经济地理,2017,37(3):86-95.
[23]张路路,郑新奇,张春晓,等.基于变权模型的唐山城市脆弱性演变预警分析[J].自然资源学报,2016,31(11):1858-1870.
[24]LI Hongmin,WANG Jianzhou,LU Haiyan,et al. Research and application of a combined model based on variable weight for short term wind speed forecasting[J]. Renewable Energy,2018,116:669-684.
[25]WU Qiang,LI Bo,CHEN Yulong. Vulnerability assessment of groundwater inrush from underlying aquifers based on variable weight model and its application[J]. Water Resources Management,2016,30(10):3331-3345.
[26]WU Qiang,ZHAO Dekang,WANG Yang,et al. Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory[J]. Hydrogeology Journal,2017,25(10):1-15.
[27]付博,姜琦刚,任春颖.扎龙湿地生态脆弱性评价与分析[J].干旱区资源与环境,2011,25(1):49-52.
[28]孙平军,修春亮,王忠芝.基于PSE模型的矿业城市生态脆弱性的变化研究———以辽宁阜新为例[J].经济地理,2010,30(8):1354-1359.
[29]李永化,范强,王雪,等.基于SRP模型的自然灾害多发区生态脆弱性时空分异研究———以辽宁省朝阳县为例[J].地理科学,2015,35(11):1452-1459.
[30]胡雅杰,董阳,郭映,等.环江流域径流模拟研究[J].资源科学,2014,36(10):2038-2045.
[31]IPCC. Climate change:the IPCC second assessment report[N]. Cambridge University Press,1996:330-355.
[32] POLSKY C,NEFF R,YARNAL B. Building comparable global change vulnerability assessments:the vulnerability scoping diagram[J]. Global Environmental Change,2007,17(3-4):472-485.
[33]SCHAUSER I,SCHNEIDERBAUER S,HARVEY A,et al.Urban regions:vulnerabilities,vulnerability assessments by indicators and adaptation options for climate change impacts,december[EB/OL].[2018-5-20]. http://www. mcrit. com/ADJUNTS/ciutats_sostenibles/UE_paper. pdf.
[34]SWART R,FONS J,GEERTSEMA W,et al. Urban vulnerability indicators a joint report of ETC-CCA and ETCSIA[J]. Social Indicators Research,2012,13(6):201-227.
[35]陈佳,杨新军,尹莎,等.基于VSD框架的半干旱地区社会———生态系统脆弱性演化与模拟[J].地理学报,2016,71(7):1172-1188.
[36]王思思,于迪,车伍.国际城市暴雨内涝脆弱性评估和适应性对策研究[J].城市发展研究,2015,22(7):23-26.
[37]吴冠岑,牛星.土地生态安全预警的惩罚型变权评价模型及应用———以淮安市为例[J].资源科学,2010,32(5):992-999.
[38]李德清,李洪兴.状态变权向量的性质与构造[J].北京师范大学学报(自然科学版),2002(4):455-461.
[39]SONG D,KEITH W H,DANG Y. Forecasting China's electricity consumption using a new grey prediction model[J]. Energy,2018,149:314-328.
[40] XU Ning,DANG Yaoguo,GONG Yande. Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China[J]. Energy,2017,118:473-380.
[41]韦师.基于灰色预测模型的我国就业发展趋势分析[J].统计与决策,2018,34(4):109-111.