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Urban agglomeration waterlogging hazard exposure assessment based on an integrated Naive Bayes classifier and complex network analysis

Author

Listed:
  • Mo Wang

    (Guangzhou University)

  • Xiaoping Fu

    (Guangzhou University
    Guangzhou University)

  • Dongqing Zhang

    (Guangdong University of Petrochemical Technology)

  • Siwei Lou

    (Guangzhou University)

  • Jianjun Li

    (Guangzhou University)

  • Furong Chen

    (Guangzhou University)

  • Shan Li

    (Guangzhou University)

  • Soon Keat Tan

    (Nanyang Technological University)

Abstract

Urban waterlogging can cause considerable economic damage, public inconvenience, and even mortality. Effective prediction of inundation probability on an urban agglomeration scale is an essential step in adaptation planning. This study proposes an urban waterlogging exposure assessment framework using the Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). WNB classifier delineates the risk distribution projections by assimilating risk factors and empirical data of urban waterlogging events. This projection was subsequently validated using an overarching accuracy coefficient of 0.85 and a Kappa coefficient of 0.75, these numerical metrics serving as critical evaluative thresholds for the verification of the WNB model's efficacy and precision. CNM is used to analyze the composition and correlation of system risk attributes according to its network topology. We applied the proposed framework to the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). We found that 20.0% of the study areas are exposed to waterlogging risk, of which 1.4% of the study areas are at high risk. There is a clear spatial concentration of urban waterlogging risks in the downtown area of populated cities. In addition, according to CNM, the urban waterlogging hazards of most townships are stressed by multiple factors such as fractional vegetation cover, impervious surface percentage, and soil water retention. The townships stressed by a single factor are attributed to the distance from the waterway or road density. The framework could provide in-depth insights into urban waterlogging preparedness and emergency response.

Suggested Citation

  • Mo Wang & Xiaoping Fu & Dongqing Zhang & Siwei Lou & Jianjun Li & Furong Chen & Shan Li & Soon Keat Tan, 2023. "Urban agglomeration waterlogging hazard exposure assessment based on an integrated Naive Bayes classifier and complex network analysis," 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. 118(3), pages 2173-2197, September.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:3:d:10.1007_s11069-023-06118-3
    DOI: 10.1007/s11069-023-06118-3
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    References listed on IDEAS

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