Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms
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...
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my.utm.868302020-09-30T09:08:42Z http://eprints.utm.my/id/eprint/86830/ Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms Yaseen, Z. M. Naganna, S. R. Sa’adi, Z. Samui, P. Ghorbani, M. A. Salih, S. Q. Shahid, S. TA Engineering (General). Civil engineering (General) 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. Springer 2020-02 Article PeerReviewed Yaseen, Z. M. and Naganna, S. R. and Sa’adi, Z. and Samui, P. and Ghorbani, M. A. and Salih, S. Q. and Shahid, S. (2020) Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms. Water Resources Management, 34 (3). pp. 1075-1091. ISSN 0920-4741 https://dx.doi.org/10.1007/s11269-020-02484-w DOI:10.1007/s11269-020-02484-w |
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TA Engineering (General). Civil engineering (General) Yaseen, Z. M. Naganna, S. R. Sa’adi, Z. Samui, P. Ghorbani, M. A. Salih, S. Q. Shahid, S. Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
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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. |
| format |
Article |
| author |
Yaseen, Z. M. Naganna, S. R. Sa’adi, Z. Samui, P. Ghorbani, M. A. Salih, S. Q. Shahid, S. |
| author_facet |
Yaseen, Z. M. Naganna, S. R. Sa’adi, Z. Samui, P. Ghorbani, M. A. Salih, S. Q. Shahid, S. |
| author_sort |
Yaseen, Z. M. |
| title |
Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| title_short |
Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| title_full |
Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| title_fullStr |
Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| title_full_unstemmed |
Hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| title_sort |
hourly river flow forecasting: application of emotional neural network versus multiple machine learning paradigms |
| publisher |
Springer |
| publishDate |
2020 |
| url |
http://eprints.utm.my/id/eprint/86830/ https://dx.doi.org/10.1007/s11269-020-02484-w |
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1680321101282934784 |
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