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Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique

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dc.citedby1
dc.contributor.authorLai V.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorKoo C.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57204919704en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57204843657en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:22Z
dc.date.available2024-10-14T03:17:22Z
dc.date.issued2023
dc.description.abstractTo ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09. � 2023, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6966
dc.identifier.doi10.1038/s41598-023-33801-z
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85156228113
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85156228113&doi=10.1038%2fs41598-023-33801-z&partnerID=40&md5=bde990902ba58b91eca26ed139da30e1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33874
dc.identifier.volume13
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectarticle
dc.subjectbee
dc.subjectdrug efficacy
dc.subjectflooding
dc.subjectgenetic algorithm
dc.subjecthawk
dc.subjectlearning
dc.subjectmetaheuristics
dc.subjectnonhuman
dc.subjectparticle swarm optimization
dc.subjectreliability
dc.subjectrisk assessment
dc.subjectvulnerability
dc.subjectwater deficit
dc.subjectwater loss
dc.subjectwater supply
dc.titleOptimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning techniqueen_US
dc.typeArticleen_US
dspace.entity.typePublication
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