Abstract
Over the past few years, urban waterlogging disasters have caused serious losses to the national economy of China; therefore, creating technology for assessing waterlogging risk levels has become an important goal. Based on 25 post-screened evaluation indexes regarding the construction of waterlogging facilities, social and economic developments, and investments in scientific and technological innovation, the capacity of 31 provinces to prevent and mitigate waterlogging was comprehensively evaluated. The scores of six principal component factors were calculated by using the entropy weight TOPSIS method, and the coupled entropy weight TOPSIS–principal component analysis evaluation model was established. Moreover, in accordance with the evaluation results, measures for waterlogging prevention and disaster reduction are proposed. The results show that Beijing, Shanghai and Tianjin are the top three provinces regarding the capacity to control floods and mitigate disasters; this agrees well with the actual flood drainage standards and disaster losses of all provinces.
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References
Alfieri L, Feyen L, Di Baldassarre G (2016) Increasing flood risk under climate change: a pan-European assessment of the benefits of four adaptation strategies. Clim Change 136(3–4):507–521. https://doi.org/10.1007/s10584-016-1641-1
Cao S, Hu Z, Luo X, Wang H (2020) Research on fault diagnosis technology of centrifugal pump blade crack based on PCA and GMM. Measurement 173:108558. https://doi.org/10.1016/j.measurement.2020.108558
Chen MF, Tzeng GH (2004) Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Math Comput Model 40(13):1473–1490. https://doi.org/10.1016/j.mcm.2005.01.006
Chen Y, Zhou H, Zhang H, Du G, Zhou J (2015) Urban flood risk warning under rapid urbanization. Environ Res 139:3–10. https://doi.org/10.1016/j.envres.2015.02.028
Chen Y, Li W, Yi P (2020) Evaluation of city innovation capability using the TOPSIS-based order relation method: the case of Liaoning province. China Technol Soc 63:101330. https://doi.org/10.1016/j.techsoc.2020.101330
Cheng S, Chang CW, Huang GH (2003) An integrated multi-criteria decision analysis and inexact integer linear programming approach for solid waste management. Eng Appl Artif Intell 16:543–554. https://doi.org/10.1016/S0952-1976(03)00069-1
Cheng WJ, Xi HY, Celestin S, Si JH, Zhao CG, Yu TF, Li AL, Wu TR (2020) Ecosystem health assessment of desert nature reserve with entropy weight and fuzzy mathematics methods: a case study of Badain Jaran Desert. Ecol Ind 119:106843. https://doi.org/10.1016/j.ecolind.2020.106843
Cui P, Li D (2019) Measuring the disaster resilience of an urban community using ANP-FCE method from the perspective of capitals. Soc Sci Q 100(6):2059–2077. https://doi.org/10.1111/ssqu.12699
de Lima Silva DF, de Almeida Filho AT (2020) Sorting with TOPSIS through boundary and characteristic profiles. Comput Ind Eng 141:106328. https://doi.org/10.1016/j.cie.2020.106328
Delgado A, Romero I (2016) Environmental conflict analysis using an integrated grey clustering and entropy-weight method: a case study of a mining project in Peru. Environ Model Softw 77:108–121. https://doi.org/10.1016/j.envsoft.2015.12.011
Gong W, Wang N, Zhang N, Han W, Qiao H (2020) Water resistance and a comprehensive evaluation model of magnesium oxychloride cement concrete based on Taguchi and entropy weight method. Constr Build Mater 260:119817. https://doi.org/10.1016/j.conbuildmat.2020.119817
Guo J, Kong F (2019) Validity analysis of historical data for probabilistic risk analysis in natural disaster. J Catastrophol 34(003):21–26. https://doi.org/10.3969/j.issn.1000-811X.2019.03.005
Hu MC, Takahiro S, Zhang XQ, Tanaka K, Takara K, Yang H (2017) Evaluation of low impact development approach for mitigating flood inundation at a watershed scale in China. J Environ Manage 193:430–438. https://doi.org/10.1016/j.jenvman.2017.02.020
Huang QJ (2007) Status Quo and Suggestions for the Construction of Beijing Rural Social Security System. Macroeconomic Management 000(005): 44-47. https://doi.org/10.19709/j.cnki.11-3199/f.2007.05.014
Hwang CL, Yoon K (1981) Multiple attribute decision making. Lecture Notes Econ Math Systems 404(4):287–288. https://doi.org/10.1007/978-3-642-48318-9
Jiang Y, Chirs Z, Ma Y (2018) Urban pluvial flooding and stormwater management: a contemporary review of China’s challenges and “sponge cities” strategy. Environ Sci Policy 80:132–143. https://doi.org/10.1016/j.envsci.2017.11.016
John W, Norma B, Dominik L, Andrew H, Mark R, Edward G, Giles H (2020) PCA of waveforms and functional PCA: a primer for biomechanics. J Biomech 116:110106. https://doi.org/10.1016/j.jbiomech.2020.110106
Jun S, Bing L (2020) Nonlinear and additive principal component analysis for functional data. J Multivar Anal 181:104675. https://doi.org/10.1016/j.jmva.2020.104675
Kaźmierczak A, Cavan G (2011) Surface water flooding risk to urban communities: analysis of vulnerability, hazard and exposure. Landsc Urban Plan 103(2):185–197. https://doi.org/10.1016/j.landurbplan.2011.07.008
Lian JJ, Xu HS, Xu K, Ma C (2017) Optimal management of the flooding risk caused by the joint occurrence of extreme rainfall and high tide level in a coastal city. Nat Hazards 59(1):183–200. https://doi.org/10.1007/s11069-017-2958-4
Lin SS, Shen SL, Zhou AN, Xu YS (2020) Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels. Water Res 187:116437. https://doi.org/10.1016/j.watres.2020.116437
Liu JH, Shao WW, Xiang CY, Mei C, Li ZJ (2019) Uncertainties of urban flood modeling: influence of parameters for different underlying surfaces. Environ Res 182:108929. https://doi.org/10.1016/j.envres.2019.108929
Quan RS (2014) Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation. Nat Hazards 73(3):1569–1585. https://doi.org/10.1007/s11069-014-1156-x
Reyhaneh S, Abbas R, Ali E (2019) Risk analysis of urban stormwater infrastructure systems using fuzzy spatial multi-criteria decision making. Sci Total Environ 647:1468–1477. https://doi.org/10.1016/j.scitotenv.2018.08.074
Shi XD, Wang JL, Yang M (2019) Review and exploration of Beijing’s master plan (2016–2035) evaluation. Urban Plan Forum 03:66–73. https://doi.org/10.16361/j.upf.201903008
Thanvisitthpon N, Shrestha S, Pal I, Ninsawat S, Chaowiwat W (2020) Assessment of flood adaptive capacity of urban areas in Thailand. Environ Impact Assess Rev 81:106363. https://doi.org/10.1016/j.eiar.2019.106363
Wang YH, Wen ZG, Li HF (2020b) Symbiotic technology assessment in iron and steel industry based on entropy TOPSIS method. J Clean Prod 260:120900. https://doi.org/10.1016/j.jclepro.2020.120900
Wang JJ, Wang CT, Zeng S (2020) Assessment of urban drainage capacity and waterlogging risk based on scenario simulation. China Water Wastewater 36(17):115–120. https://doi.org/10.19853/j.zgjsps.1000-4602.2020.17.020
Xie T, Wang M, Chao S, Chen W (2018) Evaluation of the natural attenuation capacity of urban residential soils with ecosystem-service performance index (EPX) and entropy-weight methods. Environ Pollut 238:222–229. https://doi.org/10.1016/j.envpol.2018.03.013
Xu HS, Ma C, Lian JJ, Xu K, Chaima E (2018) Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. J Hydrol 563:975–986. https://doi.org/10.1016/j.jhydrol.2018.06.060
Yin Z, Yin J, Xu SY, Wen JH (2011) Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging. J Geog Sci 21(002):274–284. https://doi.org/10.1007/s11442-011-0844-7
Yu Y, Peng MJ, Wang H, Ma ZG, Li W (2020) Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant. Ann Nucl Energy 148:107662. https://doi.org/10.1016/j.anucene.2020.107662
Zhang Q, Wu Z, Guo G, Zhang H, Tarolli P (2020) Explicit the urban waterlogging spatial variation and its driving factors: the stepwise cluster analysis model and hierarchical partitioning analysis approach. Sci Total Environ 763:143041. https://doi.org/10.1016/j.scitotenv.2020.143041
Zhang H, Cheng SQ, Li HF, Fu K, Xu Y (2020) Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in southwestern China. Sci Total Environ 741:140383. https://doi.org/10.1016/j.scitotenv.2020.140383
Zhao Y, Gong ZW, Wang WH, Luo K (2014) The comprehensive risk evaluation on rainstorm and flood disaster losses in China mainland from 2004 to 2009: based on the triangular gray correlation theory. Nat Hazards 71(2):1001–1016. https://doi.org/10.1007/s11069-013-0698-7
Zou JQ, Li PF (2020) Modelling of litchi shelf life based on the entropy weight method. Food Packag Shelf Life 25:100509. https://doi.org/10.1016/j.fpsl.2020.100509
Acknowledgements
This work was supported by the Special Fund for Postgraduate Innovation in Jiangxi Province, China [Grant No. YC2020-S125]; the Postgraduate Innovation Project of Nanchang University, China [Grant No. CX2019115].
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Liu, Z., Jiang, Z., Xu, C. et al. Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method. Nat Hazards 108, 1545–1567 (2021). https://doi.org/10.1007/s11069-021-04744-3
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DOI: https://doi.org/10.1007/s11069-021-04744-3