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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

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dc.citedby2
dc.contributor.authorAlmubaidin M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorAL-Assifeh K.A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57476845900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid58062951100en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:43:58Z
dc.date.available2025-03-03T07:43:58Z
dc.date.issued2024
dc.description.abstractRecently, there has been a growing interest in employing optimization techniques to ascertain the most efficient operation of reservoirs. This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). Each algorithm was integrated into a reservoir simulation model, focusing on finding optimal rule curves for the Mujib reservoir in Jordan from 2004 to 2019. The primary objective was to evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir. Furthermore, the study aimed to determine the effects of water demand management by reducing it by 10%, 20%, and 30%. The results revealed that the used algorithms effectively mitigated water shortages and excess releases compared to the current operational strategy. Notably, the Teaching Learning-Based Optimization (TLBO) algorithm yielded the most favorable outcomes, reducing the frequency and average of water shortages to 55.09% and 56.26%, respectively. Additionally, it curtailed the frequency and average of excess releases to 63.16% and 73.31%, respectively. ? The Author(s), under exclusive licence to Springer Nature B.V. 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-023-03716-5
dc.identifier.epage1223
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85186195557
dc.identifier.spage1207
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85186195557&doi=10.1007%2fs11269-023-03716-5&partnerID=40&md5=074a51541d4d7e087ae7cd6f4e4806f5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36693
dc.identifier.volume38
dc.pagecount16
dc.publisherSpringer Science and Business Media B.V.en_US
dc.relation.ispartofAll Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.subjectJordan
dc.subjectCurve fitting
dc.subjectGenetic algorithms
dc.subjectHeuristic algorithms
dc.subjectInvasive weed optimization
dc.subjectLearning algorithms
dc.subjectParticle swarm optimization (PSO)
dc.subjectReservoir management
dc.subjectMeta-heuristics algorithms
dc.subjectOperating policies
dc.subjectOptimization algorithms
dc.subjectOptimization techniques
dc.subjectReservoir rule curve
dc.subjectRule curves
dc.subjectSimulation model
dc.subjectStandard operating policy
dc.subjectTeaching-learning-based optimizations
dc.subjectWater shortages
dc.subjectgenetic algorithm
dc.subjectmodeling
dc.subjectreservoir
dc.subjectwater demand
dc.subjectwater management
dc.subjectReservoirs (water)
dc.titleDeriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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