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Operating a reservoir system based on the shark machine learning algorithm

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dc.citedby19
dc.contributor.authorAllawi M.F.en_US
dc.contributor.authorJaafar O.en_US
dc.contributor.authorMohamad Hamzah F.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorHossain M.S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57057678400en_US
dc.contributor.authorid6504503295en_US
dc.contributor.authorid56266163500en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T06:52:13Z
dc.date.available2023-05-29T06:52:13Z
dc.date.issued2018
dc.descriptionArtificial intelligence; Decision making; Genetic algorithms; Learning systems; Reliability analysis; Reservoirs (water); Stochastic systems; Comprehensive analysis; High dams; Mathematical optimization model; Performance indicators; Semi-arid region; Stochastic features; Water deficits; Water release; Learning algorithms; algorithm; machine learning; model; numerical model; optimization; performance assessment; reservoir; semiarid region; stochasticity; water demand; water stress; Aswan Dam; Aswan [Egypt]; Egypt; Chondrichthyesen_US
dc.description.abstractThe operating process of a multi-purpose reservoir needs to develop models that have the ability to overcome the challenges facing the decision makers. Therefore, the development of a mathematical optimization model is crucial for selecting the optimal policies for the reservoir operation. In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. The SMLA began with a group of randomly produced potential solutions and later interactively executed the search for the optimal solution. The procedure for the SMLA is suitable to be applied to a reservoir system due to its ability to tackle the stochastic features of dam and reservoir systems. The major purpose of the proposed models is to generate an operation rule that could minimize the absolute value of the differences between water release and water demand. The proposed model has been examined using the data of the Aswan High Dam, Egypt as the case study. The performance of the SMLA was compared with the performance of the most widespread evolutionary algorithms, namely, the genetic algorithm (GA). Comprehensive analysis of the results was performed using three performance indicators, namely, resilience, reliability, and vulnerability. This work concluded that the performance of the SMLA model was better than the GA model in generating the optimal policy for reservoir operation. The result showed that the SMLA succeeded in providing high reliability (99.72%), significant resilience (1) and minimum vulnerability (20.7% of demand). � 2018, Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo366
dc.identifier.doi10.1007/s12665-018-7546-8
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85046870288
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046870288&doi=10.1007%2fs12665-018-7546-8&partnerID=40&md5=d641dac21d634c973ff01bed0e41f684
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23829
dc.identifier.volume77
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Earth Sciences
dc.titleOperating a reservoir system based on the shark machine learning algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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