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Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience

Author

Listed:
  • The Pham

    (Van Lang University
    Van Lang University)

  • Dung Xuan Bui

    (Vietnam National University of Forestry)

  • Tuyet Anh Thi Do

    (Hanoi University of Natural Resources and Environment)

  • Anh Ngoc Thi Do

    (Van Lang University
    Van Lang University)

Abstract

Flooding is a critical global issue, significantly impacting sustainable development and urban health, particularly in rapidly urbanizing regions. In Vietnam, flooding poses severe challenges, with Hanoi being notably affected due to its rapid urban expansion and reduction of green spaces. This study evaluates flood susceptibility in Hanoi using high-resolution remote sensing imagery integrated with machine learning models. The Artificial Neural Network (ANN) model, optimized with a Genetic Algorithm (GA), demonstrated superior performance (R2test = 0.823; RMSE = 4.332; and MAE = 4.020). The resulting flood susceptibility map highlights stark contrasts between urban and suburban areas. Urban districts such as Cau Giay, Nam Tu Liem, Ha Dong, and Thanh Xuan exhibit high to very high flood risks due to dense construction, high population density, and proximity to rivers. Conversely, suburban areas generally show lower susceptibility, except for densely developed regions like Thach That and Quoc Oai districts. These findings underscore the need for comprehensive flood sensitivity mapping across temporal and spatial dimensions to inform urban planning and management. This research provides a valuable tool for early flood risk detection and supports policymakers in making informed decisions to enhance urban health and resilience.

Suggested Citation

  • The Pham & Dung Xuan Bui & Tuyet Anh Thi Do & Anh Ngoc Thi Do, 2025. "Assessing flood susceptibility in Hanoi using machine learning and remote sensing: implications for urban health and resilience," 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. 121(9), pages 10149-10170, May.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:9:d:10.1007_s11069-025-07211-5
    DOI: 10.1007/s11069-025-07211-5
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    References listed on IDEAS

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    1. Romulus Costache, 2019. "Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3239-3256, July.
    2. Fatemeh Jalayer & Raffaele Risi & Francesco Paola & Maurizio Giugni & Gaetano Manfredi & Paolo Gasparini & Maria Topa & Nebyou Yonas & Kumelachew Yeshitela & Alemu Nebebe & Gina Cavan & Sarah Lindley , 2014. "Probabilistic GIS-based method for delineation of urban flooding risk hotspots," 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(2), pages 975-1001, September.
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