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Reservoir water balance simulation model utilizing machine learning algorithm

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dc.citedby18
dc.contributor.authorDashti Latif S.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:09:15Z
dc.date.available2023-05-29T09:09:15Z
dc.date.issued2021
dc.descriptionDigital storage; Forecasting; Learning algorithms; Machine learning; Mean square error; Neural networks; Predictive analytics; Water levels; Water quality; ANN prediction; High-efficiency; Radial Basis Function(RBF); Reservoir operation; Reservoir water; Root mean square errors; Test Modeling; Variation of water level; Reservoirs (water)en_US
dc.description.abstractDeveloping water losses and reservoir final storage forecast has become an increasingly important task for reservoir operation. Accurate forecasts would lead to better monitoring of water quality and more efficient reservoir operation. Therefore, the flash flood and water crisis problems in Malaysia can be reduced. Artificial neural networks (ANN) models with radial basis function (RBF) have been determined for high efficiency and accuracy, especially in the dynamics system. In this study, the proposed ANN Prediction Model is being developed by using inflow, the release of dam, initial and final storage of the reservoir as input, whereas the water losses from the reservoir as output. All the data collected over 11 years (1997�2007) at Klang Gate reservoir has been used to develop and test model output. The results indicated that the proposed model could provide monthly forecasting with maximum root mean square error of � 20.07%. The advantages of this ANN model are to provide information for water losses, final storage, and variation of water level for better reservoir operation. � 2020 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2020.10.057
dc.identifier.epage1378
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85095820836
dc.identifier.spage1365
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85095820836&doi=10.1016%2fj.aej.2020.10.057&partnerID=40&md5=90ebfeac6184daced41a1b4a8720356c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26335
dc.identifier.volume60
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.titleReservoir water balance simulation model utilizing machine learning algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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