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A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm

Author

Listed:
  • Nie Zhou

    (Wuhan University)

  • Jingming Hou

    (Xi’an University of Technology)

  • Hua Chen

    (Wuhan University)

  • Guangzhao Chen

    (Xi’an University of Technology)

  • Bingyi Liu

    (Wuhan University)

Abstract

Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (KNN) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods.

Suggested Citation

  • Nie Zhou & Jingming Hou & Hua Chen & Guangzhao Chen & Bingyi Liu, 2024. "A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and KNN Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1903-1919, April.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-023-03664-0
    DOI: 10.1007/s11269-023-03664-0
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    References listed on IDEAS

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    1. You-Da Jhong & Hsin-Ping Lin & Chang-Shian Chen & Bing-Chen Jhong, 2022. "Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3221-3245, 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.
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