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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network

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dc.citedby10
dc.contributor.authorZanial W.N.C.W.en_US
dc.contributor.authorMalek M.B.A.en_US
dc.contributor.authorReba M.N.M.en_US
dc.contributor.authorZaini N.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57205239441en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid57222067435en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:21:26Z
dc.date.available2024-10-14T03:21:26Z
dc.date.issued2023
dc.description.abstractOne of the largest hydropower facilities currently in operation in Malaysia is the Terengganu hydroelectric facility. As a result, for hydropower generation to be sustainable, future water availability in hydropower plants must be known. Therefore, it is necessary to precisely estimate how the river flow will alter as a result of changing rainfall patterns. Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. In this research, Artificial Neural Network (ANN) is integrated with a nature-inspired optimizer, namely Cuckoo search algorithm (CS-ANN). The performance of the proposed algorithm then will be examined based on statistical indices namely Root-Mean-Square Error (RSME) and Determination Coefficient (R2). Then, the accuracy of the proposed model will be then examined with the stand-alone Artificial Neural Network (ANN). The statistical indices results indicate that the proposed Hybrid CS-ANN model showed an improvement based on R2 value as compared to ANN model with R2 of 0.900 at training stage and R2 of 0.935 at testing stage. RMSE value, for ANN model, is 127.79 m3/s for training stage and 12.7 m3/s at testing stage. While for the proposed Hybrid CS-ANN model, RMSE value is equal to 121.7 m3/s for training stage and 10.95 m3/s for testing stage. The results revealed that the proposed model outperformed the stand-alone model in predicting the river flow with high level of accuracy. Although the proposed model could be applied in different case study, there is a need to tune the model internal parameters when applied in different case study. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo28
dc.identifier.doi10.1007/s13201-022-01830-0
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85143329279
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85143329279&doi=10.1007%2fs13201-022-01830-0&partnerID=40&md5=65d1a13be48b17ab91946931e8c58b6b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34651
dc.identifier.volume13
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleApplied Water Science
dc.subjectANN
dc.subjectHybrid CS-ANN
dc.subjectHydropower Plant
dc.subjectRiver flow
dc.subjectMalaysia
dc.subjectBiomimetics
dc.subjectHydroelectric power
dc.subjectHydroelectric power plants
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectRivers
dc.subjectStream flow
dc.subjectArtificial neural network modeling
dc.subjectCase-studies
dc.subjectHybrid CS-artificial neural network
dc.subjectHydropower plants
dc.subjectMachine learning methods
dc.subjectPrediction-based
dc.subjectRiver flow
dc.subjectRiver flow prediction
dc.subjectStand -alone
dc.subjectStatistical indices
dc.subjectalgorithm
dc.subjectartificial neural network
dc.subjecthydroelectric power plant
dc.subjectmachine learning
dc.subjectprecipitation intensity
dc.subjectprediction
dc.subjectriver flow
dc.subjectMean square error
dc.titleRiver flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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