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Application of intelligent optimization techniques and investigating the effect of reservoir size in calibrating the reservoir operating policy

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dc.citedby6
dc.contributor.authorHossain Md.S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorMohtar W.H.M.W.en_US
dc.contributor.authorid55579596900en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid25637975300en_US
dc.date.accessioned2023-05-29T05:59:38Z
dc.date.available2023-05-29T05:59:38Z
dc.date.issued2015
dc.description.abstractIn this study, we applied the most recently developed artificial bee colony (ABC) optimization technique in search of an optimal reservoir release policy. The effect of the optimization algorithms was also investigated in terms of reservoir size and operational complexities. Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are used to compare the model performances. Two different reservoir data were used to achieve the detailed analysis and complete understanding of the application efficiency of these optimization techniques. Release curves were developed for every month as guidance for the decisionmaker. Simulation was carried out for each method using actual inflow data, and reliability, resiliency and vulnerability are measured. The release policy provided by ABC optimization algorithms outperformed in terms of reliability, less waste of water and handling critical situations of low inflow. Also, the ABC showed better performance in the case of complex reservoirs. � 2015 IWA Publishing.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/wp.2015.023
dc.identifier.epage1162
dc.identifier.issue6
dc.identifier.scopus2-s2.0-84955473434
dc.identifier.spage1143
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84955473434&doi=10.2166%2fwp.2015.023&partnerID=40&md5=c3ba0ed1deddaf4598db661887bf3ce2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22208
dc.identifier.volume17
dc.publisherIWA Publishingen_US
dc.sourceScopus
dc.sourcetitleWater Policy
dc.titleApplication of intelligent optimization techniques and investigating the effect of reservoir size in calibrating the reservoir operating policyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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