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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Institution: Universiti Tenaga Nasional
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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