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

dc.citedby131
dc.contributor.authorTikhamarine Y.en_US
dc.contributor.authorSouag-Gamane D.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorKisi O.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57210575507en_US
dc.contributor.authorid55363629300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid6507051085en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:10:59Z
dc.date.available2023-05-29T08:10:59Z
dc.date.issued2020
dc.descriptionArtificial 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]; Egypten_US
dc.description.abstractMonthly 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo124435
dc.identifier.doi10.1016/j.jhydrol.2019.124435
dc.identifier.scopus2-s2.0-85076948206
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85076948206&doi=10.1016%2fj.jhydrol.2019.124435&partnerID=40&md5=8fe570772d23b1421a3f337c2da949b2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25565
dc.identifier.volume582
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.titleImproving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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