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Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method

Author

Listed:
  • Zaohong Liu

    (Nanchang University)

  • Zhangzejun Jiang

    (Nanchang University)

  • Chen Xu

    (Nanchang University)

  • Guanjun Cai

    (Nanchang University)

  • Jian Zhan

    (Nanchang University)

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.

Suggested Citation

  • Zaohong Liu & Zhangzejun Jiang & Chen Xu & Guanjun Cai & Jian Zhan, 2021. "Assessment of provincial waterlogging risk based on entropy weight TOPSIS–PCA method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 1545-1567, September.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:2:d:10.1007_s11069-021-04744-3
    DOI: 10.1007/s11069-021-04744-3
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    References listed on IDEAS

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    1. Rui-Song Quan, 2014. "Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(3), pages 1569-1585, September.
    2. Chen, Yang & Li, Weiwei & Yi, Pingtao, 2020. "Evaluation of city innovation capability using the TOPSIS-based order relation method: The case of Liaoning province, China," Technology in Society, Elsevier, vol. 63(C).
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    4. Peng Cui & Dezhi Li, 2019. "Measuring the Disaster Resilience of an Urban Community Using ANP‐FCE Method from the Perspective of Capitals," Social Science Quarterly, Southwestern Social Science Association, vol. 100(6), pages 2059-2077, October.
    5. Jijian Lian & Hongshi Xu & Kui Xu & Chao Ma, 2017. "Optimal management of the flooding risk caused by the joint occurrence of extreme rainfall and high tide level in a coastal city," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 183-200, October.
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    Cited by:

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    5. Zhang, Mingming & Zhou, Simei & Wang, Qunwei & Liu, Liyun & Zhou, Dequn, 2023. "Will the carbon neutrality target impact China's energy security? A dynamic Bayesian network model," Energy Economics, Elsevier, vol. 125(C).

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