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

Zaher Mundher Yaseen (), Sujay Raghavendra Naganna (), Zulfaqar Sa’adi (), Pijush Samui (), Mohammad Ali Ghorbani (), Sinan Q. Salih () and Shamsuddin Shahid ()
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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
Sinan Q. Salih: Duy Tan University
Shamsuddin Shahid: Universiti Teknologi Malaysia

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 3, No 9, 1075-1091

Abstract: 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.

Keywords: Albert river; Emotional neural network; Machine learning; River flow; Time series forecasting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-020-02484-w

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