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Improving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration model

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dc.citedby10
dc.contributor.authorFayaed S.S.en_US
dc.contributor.authorFiyadh S.S.en_US
dc.contributor.authorKhai W.J.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorIbrahim R.K.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorKoting S.en_US
dc.contributor.authorMohd N.S.en_US
dc.contributor.authorBinti Jaafar W.Z.en_US
dc.contributor.authorHin L.S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid54782522900en_US
dc.contributor.authorid57197765961en_US
dc.contributor.authorid57211320170en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57188832586en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid55839645200en_US
dc.contributor.authorid57192892703en_US
dc.contributor.authorid57208880526en_US
dc.contributor.authorid57201523473en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T07:23:27Z
dc.date.available2023-05-29T07:23:27Z
dc.date.issued2019
dc.descriptionartificial neural network; dam; integrated approach; model; reservoir; simulation; stochasticity; water relations; water resourceen_US
dc.description.abstractThe simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively. � 2019 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5367
dc.identifier.doi10.3390/su11195367
dc.identifier.issue19
dc.identifier.scopus2-s2.0-85073418129
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073418129&doi=10.3390%2fsu11195367&partnerID=40&md5=1bf64d462ba374ebeef36ae31df23561
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24428
dc.identifier.volume11
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleImproving dam and reservoir operation rules using stochastic dynamic programming and artificial neural network integration modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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