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Conceptual Sim-Heuristic optimization algorithm to evaluate the climate impact on reservoir operations

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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.authorNajah Ahmed A.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.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:36:13Z
dc.date.available2023-05-29T09:36:13Z
dc.date.issued2022
dc.descriptionClimate change; Climate models; Digital storage; Neural networks; Reservoirs (water); Water resources; Coupled Model Intercomparison Project; Coupled model intercomparison project 5; Down-scaling; General circulation model; Klang gate dam; Metaheuristic; Optimisations; Reservoir operation; Simulation; Support vector regressions; Optimization; climate change; climate effecten_US
dc.description.abstractThis study covers the application of sim-heuristics to simulate and optimise the KLang Gate Dam (KGD) operating rule curve using the Coupled Model Intercomparison Project 5 (CMIP5) climate scenarios. This research aims to examine future climate change impacts on the KGD reservoir water resources. First, based on model institution location and data availability, a few General Circulation Models (GCMs) under the CMIP5 were chosen. Most earlier studies had solely examined the impact of climate change on future reservoir operations using a single GCM. The ensemble of GCMs for precipitation, temperature (Maximum, Minimum, and Mean), and solar radiation for the base period (1991�2005) and future climatic scenarios under the Representative Concentration Pathways, RCP 2.6, RCP 4.5, and RCP 8.5 were downscaled, trained, and tested using data-driven techniques namely; the Artificial Neural Network (ANN) and the Support Vector Regression (SVR). During the base period, the SVR (Poly function) achieved R performance values of 0.6201, 0.5743, 0.6926, and 0.6073 for the respective predictant variables. Upon addressing for rainfall-runoff, the Turc-radiation evaporation strategy was utilised at this study location since it was suitable for the tropical, humid, or sub-humid region. Few scenarios were developed to forecast water demand. Scenario 1 was based on the base period (1991�2005) of water demand, whereas Scenarios 2 and 3 were based on maximum and mean temperatures, respectively (2020�2099). The results were then evaluated in terms of storage failure, reliability, resilience, and vulnerability. Overall, Scenario 3 showed the greatest reliability in satisfying exact demand with 93.54 %, as well as the least shortage index and length of water deficit under RCP 4.5. � 2022 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo128530
dc.identifier.doi10.1016/j.jhydrol.2022.128530
dc.identifier.scopus2-s2.0-85139876528
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139876528&doi=10.1016%2fj.jhydrol.2022.128530&partnerID=40&md5=887db40112f07b36bdc2190911bc7bb3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26694
dc.identifier.volume614
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleJournal of Hydrology
dc.titleConceptual Sim-Heuristic optimization algorithm to evaluate the climate impact on reservoir operationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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