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

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.

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Acknowledgements

The authors thank Associate Professor Ravinesh Deo for providing the river flow data and his constructive comments on establishing this research. In addition, we do acknowledge the valuable comments reported by the respected reviewers to enhance the manuscript presentation and readability. Further, our appreciation is extended to the editor-in-chief and the associated editor for handling our manuscript.

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Correspondence to Sinan Q. Salih.

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Yaseen, Z.M., Naganna, S.R., Sa’adi, Z. et al. Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms. Water Resour Manage (2020). https://doi.org/10.1007/s11269-020-02484-w

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Keywords

  • Albert river
  • Emotional neural network
  • Machine learning
  • River flow
  • Time series forecasting