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Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

Artificial intelligence; Estimation; Evolutionary algorithms; Forecasting; Heuristic algorithms; Hybrid systems; Optimization; Water resources; Comprehensive analysis; High dams; Meta heuristic algorithm; Modelling techniques; Performance indicators; Streamflow forecasting; Training and testing; Wat...

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Main Authors:	Tikhamarine Y., Souag-Gamane D., Najah Ahmed A., Kisi O., El-Shafie A.
Other Authors:	57210575507
Format:	Article
Published:	Elsevier B.V. 2023
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id	my.uniten.dspace-25565
record_format	dspace
spelling	my.uniten.dspace-255652023-05-29T16:10:59Z Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm Tikhamarine Y. Souag-Gamane D. Najah Ahmed A. Kisi O. El-Shafie A. 57210575507 55363629300 57214837520 6507051085 16068189400 Artificial intelligence; Estimation; Evolutionary algorithms; Forecasting; Heuristic algorithms; Hybrid systems; Optimization; Water resources; Comprehensive analysis; High dams; Meta heuristic algorithm; Modelling techniques; Performance indicators; Streamflow forecasting; Training and testing; Water resources management; Stream flow; artificial intelligence; forecasting method; genetic algorithm; optimization; precision; streamflow; Aswan Dam; Aswan [Egypt]; Egypt Monthly streamflow forecasting is required for short- and long-term water resources management especially in extreme events such as flood and drought. Therefore, there is need to develop a reliable and precise model for streamflow forecasting. The precision of Artificial Intelligence (AI) models can be improved by using hybrid AI models which consist of coupled models. Therefore, the chief aim of this study is to propose efficient hybrid system by integrating Grey Wolf Optimization (GWO) algorithm with Artificial Intelligence (AI) models. 130 years of monthly historical natural streamflow data will be used to evaluate the performance of the proposed modelling technique. Quantitative performance indicators will be introduced to evaluate the validity of the integrated models; in addition to that, comprehensive analysis will be conducted between the predicted and the observed streamflow. The results show the integrated AI with GWO outperform the standard AI methods and can make better forecasting during training and testing phases for the monthly inflow in all input cases. This finding reveals the superiority of GWO meta-heuristic algorithm in improving the accuracy of the standard AI in forecasting the monthly inflow. � 2019 Elsevier B.V. Final 2023-05-29T08:10:59Z 2023-05-29T08:10:59Z 2020 Article 10.1016/j.jhydrol.2019.124435 2-s2.0-85076948206 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076948206&doi=10.1016%2fj.jhydrol.2019.124435&partnerID=40&md5=8fe570772d23b1421a3f337c2da949b2 https://irepository.uniten.edu.my/handle/123456789/25565 582 124435 Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Artificial intelligence; Estimation; Evolutionary algorithms; Forecasting; Heuristic algorithms; Hybrid systems; Optimization; Water resources; Comprehensive analysis; High dams; Meta heuristic algorithm; Modelling techniques; Performance indicators; Streamflow forecasting; Training and testing; Water resources management; Stream flow; artificial intelligence; forecasting method; genetic algorithm; optimization; precision; streamflow; Aswan Dam; Aswan [Egypt]; Egypt
author2	57210575507
author_facet	57210575507 Tikhamarine Y. Souag-Gamane D. Najah Ahmed A. Kisi O. El-Shafie A.
format	Article
author	Tikhamarine Y. Souag-Gamane D. Najah Ahmed A. Kisi O. El-Shafie A.
spellingShingle	Tikhamarine Y. Souag-Gamane D. Najah Ahmed A. Kisi O. El-Shafie A. Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
author_sort	Tikhamarine Y.
title	Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
title_short	Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
title_full	Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
title_fullStr	Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
title_full_unstemmed	Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm
title_sort	improving artificial intelligence models accuracy for monthly streamflow forecasting using grey wolf optimization (gwo) algorithm
publisher	Elsevier B.V.
publishDate	2023
_version_	1806428130105098240
score	13.222552

Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

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