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Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms

Author

Listed:
  • Zaher Mundher Yaseen

    (Ton Duc Thang University)

  • Sujay Raghavendra Naganna

    (Department of Civil Engineering, Shri Madhwa Vadiraja Institute of Technology and Management)

  • Zulfaqar Sa’adi

    (Universiti Teknologi Malaysia)

  • Pijush Samui

    (Department of Civil Engineering, National Institute of Technology Patna)

  • Mohammad Ali Ghorbani

    (University of Tabriz
    Near East University)

  • Sinan Q. Salih

    (Duy Tan University
    University of Anbar)

  • Shamsuddin Shahid

    (Universiti Teknologi Malaysia)

Abstract

Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using various machine learning (ML) based models including a relatively new and novel artificial intelligent modeling technique known as emotional neural network (ENN). Hourly river flow data for the period 2011–2014 is employed for the development and evaluation of the predictive models. The performance of the ENN model in forecasting hourly stage river flow is compared with other well-established ML-based models using a number of statistical metrics and graphical evaluation methods. The ENN showed an outstanding performance in terms of their forecasting accuracies, in comparison with other ML models. In general, the results clearly advocate the ENN as a promising artificial intelligence technique for accurate forecasting of hourly river flow in the form of real-time.

Suggested Citation

  • Zaher Mundher Yaseen & Sujay Raghavendra Naganna & Zulfaqar Sa’adi & Pijush Samui & Mohammad Ali Ghorbani & Sinan Q. Salih & Shamsuddin Shahid, 2020. "Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1075-1091, February.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:3:d:10.1007_s11269-020-02484-w
    DOI: 10.1007/s11269-020-02484-w
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    References listed on IDEAS

    as
    1. Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, vol. 83(Mar), pages 39-49.
    2. Elnaz Sharghi & Vahid Nourani & Hessam Najafi & Amir Molajou, 2018. "Emotional ANN (EANN) and Wavelet-ANN (WANN) Approaches for Markovian and Seasonal Based Modeling of Rainfall-Runoff Process," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3441-3456, August.
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    Citations

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    Cited by:

    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    2. Hossien Riahi-Madvar & Majid Dehghani & Rasoul Memarzadeh & Bahram Gharabaghi, 2021. "Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1149-1166, March.
    3. Ana C. Cebrián & Ricardo Salillas, 2021. "Forecasting High-Frequency River Level Series Using Double Switching Regression with ARMA Errors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 299-313, January.
    4. Amir Molajou & Vahid Nourani & Abbas Afshar & Mina Khosravi & Adam Brysiewicz, 2021. "Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2369-2384, June.

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