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Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model

Author

Listed:
  • Jian Chen

    (Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Yaowei Li

    (Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Changhui Zhang

    (Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Yangyang Tian

    (Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Zhikai Guo

    (Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

Abstract

At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow and it is difficult to meet the needs of daily flood control and emergency. How to use artificial intelligence technology to quickly predict urban flooding is a key concern and a problem that needs to be solved. Therefore, this paper combines a numerical model with good computational accuracy and an LSTM artificial neural network model with high computational efficiency to propose a new method for fast prediction of urban flooding risk. The method uses the simulation results of the numerical model of urban flooding as the data driver to construct the LSTM neural network prediction model of each waterlogging point. The results show that the method has a high prediction accuracy and fast calculation speed, which can meet the needs of daily flood control and emergency response, and provides a new idea for the application of artificial intelligence technology in the direction of flood prevention and mitigation.

Suggested Citation

  • Jian Chen & Yaowei Li & Changhui Zhang & Yangyang Tian & Zhikai Guo, 2023. "Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model," IJERPH, MDPI, vol. 20(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1043-:d:1027302
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    Citations

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    Cited by:

    1. Syed Asad Shabbir Bukhari & Imran Shafi & Jamil Ahmad & Santos Gracia Villar & Eduardo Garcia Villena & Tahir Khurshaid & Imran Ashraf, 2025. "Review of flood monitoring and prevention approaches: a data analytic perspective," 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(5), pages 5103-5128, March.
    2. Syed Asad Shabbir Bukhari & Imran Shafi & Jamil Ahmad & Hammad Tanveer Butt & Tahir Khurshaid & Imran Ashraf, 2025. "Enhancing flood monitoring and prevention using machine learning and IoT integration," 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(4), pages 4837-4864, March.

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