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Solving flood problems with deep learning technology: Research status, strategies, and future directions

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  • Hongyang Li
  • Mingxin Zhu
  • Fangxin Li
  • Martin Skitmore

Abstract

As a frequent and devastating natural disaster worldwide, floods are influenced by complex factors. Building flood models for simulating, monitoring, and forecasting floods is crucial to reduce the risk of disasters and minimize damage to people and property. With advancements in computing power and the impressive capabilities of deep learning in such areas as classification and prediction, there has been growing interest in using this technology in flood research. There is also a growing body of research into building flood data‐driven models with deep learning. Based on this, this study adopts a mixed‐method approach of bibliometric and qualitative analyses to provide an overview of the research. The research status is revealed in a bibliometric visualization, where the research objects are defined from the flood perspective, and the research strategies are explained from the deep learning perspective to provide a comprehensive and in‐depth understanding of the flood problem and how to apply deep learning to solve it. In addition, the study reflects on the future direction of improvement and innovation needed to promote the further development and exploration of deep learning in flood research.

Suggested Citation

  • Hongyang Li & Mingxin Zhu & Fangxin Li & Martin Skitmore, 2024. "Solving flood problems with deep learning technology: Research status, strategies, and future directions," Sustainable Development, John Wiley & Sons, Ltd., vol. 32(6), pages 7011-7035, December.
    • Handle: RePEc:wly:sustdv:v:32:y:2024:i:6:p:7011-7035
    DOI: 10.1002/sd.3074
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    References listed on IDEAS

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    1. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
    2. Junhao Wu & Zhaocai Wang & Yuan Hu & Sen Tao & Jinghan Dong, 2023. "Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 937-953, January.
    3. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
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