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Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

Author

Listed:
  • Shujie Zou

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

  • Chiawei Chu

    (Faculty of Data Science, City University of Macau, Macau 999078, China)

  • Weijun Dai

    (Artificial Intelligence College, Guangdong Polytechnic Institute, Guangzhou 510091, China)

  • Ning Shen

    (Department of Innovation, Technology and Entrepreneurship, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Jia Ren

    (School of Information and Communication Engineering, Hainan University, Haikou 570100, China)

  • Weiping Ding

    (School of Information Science and Technology, Nantong University, Nantong 226000, China)

Abstract

A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model.

Suggested Citation

  • Shujie Zou & Chiawei Chu & Weijun Dai & Ning Shen & Jia Ren & Weiping Ding, 2024. "Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis," Mathematics, MDPI, vol. 12(2), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:340-:d:1322847
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    References listed on IDEAS

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    1. Wim Kellens & Teun Terpstra & Philippe De Maeyer, 2013. "Perception and Communication of Flood Risks: A Systematic Review of Empirical Research," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 24-49, January.
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    Cited by:

    1. Yongshuai Liang & Weihong Liao & Hao Wang, 2025. "Efficient Urban Flooding Management: A Multi-Physical-Process-Oriented Flood Modelling and Analysis Method," Sustainability, MDPI, vol. 17(3), pages 1-21, January.

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