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Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction

Author

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  • Salimeh Malekpour Heydari

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia)

  • Teh Noranis Mohd Aris

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia)

  • Razali Yaakob

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia)

  • Hazlina Hamdan

    (Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Seri Kembangan 43400, Malaysia)

Abstract

The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.

Suggested Citation

  • Salimeh Malekpour Heydari & Teh Noranis Mohd Aris & Razali Yaakob & Hazlina Hamdan, 2021. "Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction," Sustainability, MDPI, vol. 13(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11537-:d:659659
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    References listed on IDEAS

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    1. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
    2. Dorel Paraschiv & Cristiana Tudor & Radu Petrariu, 2015. "The Textile Industry and Sustainable Development: A Holt–Winters Forecasting Investigation for the Eastern European Area," Sustainability, MDPI, vol. 7(2), pages 1-12, January.
    3. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks," Sustainability, MDPI, vol. 10(4), pages 1-17, April.
    4. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
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    1. Andrei M. Bramm & Pavel V. Matrenin & Alexandra I. Khalyasmaa, 2025. "A Review of XAI Methods Applications in Forecasting Runoff and Water Level Hydrological Tasks," Mathematics, MDPI, vol. 13(17), pages 1-26, September.

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