Publication:
Modeling reservoir water release decision using Adaptive Neuro Fuzzy Inference System

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dc.citedby1
dc.contributor.affiliationsUniversiti Utara Malaysia (UUM)
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorMokhtar S.A.en_US
dc.contributor.authorWan Ishak W.H.en_US
dc.contributor.authorNorwawi N.M.en_US
dc.date.accessioned2024-05-29T01:56:17Z
dc.date.available2024-05-29T01:56:17Z
dc.date.issued2016
dc.description.abstractReservoir water release decision is one of the critical actions in determining the quantity of water to be retained or released from the reservoir. Typically, the decision is influenced by the reservoir inflow that can be estimated based on the rainfall recorded at the reservoir's upstream areas. Since the rainfall is recorded at several different locations, the use of temporal pattern alone may not be appropriate. Hence, in this study a spatial temporal pattern was used to retain the spatial information of the rainfall's location. In addition, rainfall recorded at different locations may cause fuzziness in the data representation. Therefore, a hybrid computational intelligence approach, namely the Adaptive Neuro Fuzzy Inference System (ANFIS), was used to develop a reservoir water release decision model. ANFIS integrates both the neural network and fuzzy logic principles in order to deal with the fuzziness and complexity of the spatial temporal pattern of rainfall. In this study, the Timah Tasoh reservoir and rainfall from five upstream gauging stations were used as a case study. Two ANFIS models were developed and their performances were compared based on the lowest square error achieved from the simulation conducted. Both models utilized the spatial temporal pattern of the rainfall as input. The first model considered the current reservoir water level as an additional input, while the second model retained the existing input. The result indicated that the application of ANFIS could be used successfully for modeling reservoir water release decision. The first model with the additional input showed better performance with the lowest square error compared to the second model.
dc.description.natureFinalen_US
dc.identifier.epage152
dc.identifier.issn1675414X
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85010224337
dc.identifier.spage141
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85010224337&partnerID=40&md5=963fe495466e73265f546a4673715742
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9819
dc.identifier.volume15
dc.languageEnglish
dc.language.isoen_US
dc.publisherUniversiti Utara Malaysia Pressen_US
dc.relation.ispartofJournal of Information and Communication Technology
dc.sourceScopus
dc.subjectANFISen_US
dc.subjectDecision modelingen_US
dc.subjectFuzzy logicen_US
dc.subjectHybrid computational intelligenceen_US
dc.subjectNeural networken_US
dc.subjectReservoir operationen_US
dc.titleModeling reservoir water release decision using Adaptive Neuro Fuzzy Inference System
dc.typeArticleen_US
dspace.entity.typePublication

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