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Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data

Author

Listed:
  • Pin-Chun Huang

    (National Taiwan Ocean University)

  • Kuo-Lin Hsu

    (University of California, Irvine)

  • Kwan Tun Lee

    (National Taiwan Ocean University)

Abstract

The stability and efficiency of a rainfall–runoff model are of concern for establishing a flood early warning system. To tackle any problems associated with the numerical instability or computational cost of conducting a real-time runoff prediction, the neural network (NN) method has emerged as an alternative to calculate the overland-flow depths in a watershed. Therefore, instead of developing a new algorithm of machine learning to improve the predicted accuracy, this study focuses on thoroughly exploring the influence of input data that are highly related to the flow responses in space, and then establishing a procedure to process all the input data for the NN training. The novelty of this study is as follows: (1) To improve the overall accuracy of the 2D flood prediction, geomorphological factors, such as the hydrologic length (L), the flow accumulation value (FAV), and the bed slope (S) at the location of each element extracted from the topographic dataset were considered together and were classified into multiple zones for separate trainings. (2) An optimal length of the effective rainfall condition (To) was proposed by conducting a correlation analysis to determine the most informative precipitation data. In this study, the outcomes of four types of NN models were examined and compared with one another. The results show that the simplest structure of the NN methods could achieve satisfactory predictions of flow depth, as long as the approaches of data preprocessing and model training proposed in this study were implemented.

Suggested Citation

  • Pin-Chun Huang & Kuo-Lin Hsu & Kwan Tun Lee, 2021. "Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1079-1100, February.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:3:d:10.1007_s11269-021-02776-9
    DOI: 10.1007/s11269-021-02776-9
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. Thendiyath Roshni & Madan K. Jha & Ravinesh C. Deo & A. Vandana, 2019. "Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(7), pages 2381-2397, May.
    3. Hsi-Ting Fang & Bing-Chen Jhong & Yih-Chi Tan & Kai-Yuan Ke & Mo-Hsiung Chuang, 2019. "A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 797-818, January.
    4. Fereshteh Modaresi & Shahab Araghinejad & Kumars Ebrahimi, 2018. "A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasti," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 243-258, January.
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    Cited by:

    1. Ahmed A. Lamri & Said M. Easa, 2022. "Lambert W-function Solution for Uniform Flow Depth Problem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2653-2663, June.
    2. V. Gholami & M. R. Khaleghi & S. Pirasteh & Martijn J. Booij, 2022. "Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 451-469, January.

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