Publication:
Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia

Simple item page
dc.citedby5
dc.contributor.authorWee W.J.en_US
dc.contributor.authorChong K.L.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMalek M.B.A.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57226181151en_US
dc.contributor.authorid57208482172en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:21:36Z
dc.date.available2024-10-14T03:21:36Z
dc.date.issued2023
dc.description.abstractHydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103�m3/s for training and 0.143�m3/s for testing as compared to ANN standalone training and testing yielding 0.091�m3/s and 0.116�m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work�s performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo30
dc.identifier.doi10.1007/s13201-022-01831-z
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85144222517
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144222517&doi=10.1007%2fs13201-022-01831-z&partnerID=40&md5=bca1d467a317e3a958d7bf4acd2d76c6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34671
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.subjectArtificial neural network
dc.subjectBat meta-heuristic algorithm
dc.subjectStreamflow forecasting
dc.subjectUncertainty analysis
dc.subjectMalaysia
dc.subjectFlood control
dc.subjectForecasting
dc.subjectHeuristic algorithms
dc.subjectHeuristic methods
dc.subjectNeural networks
dc.subjectStream flow
dc.subjectArtificial neural network modeling
dc.subjectBat algorithms
dc.subjectBat meta-heuristic algorithm
dc.subjectFlood waters
dc.subjectMalaysia
dc.subjectMeta-heuristics algorithms
dc.subjectPerformance
dc.subjectRiver inflow
dc.subjectStreamflow forecasting
dc.subjectStudy areas
dc.subjectalgorithm
dc.subjectartificial neural network
dc.subjectforecasting method
dc.subjectinflow
dc.subjectriver flow
dc.subjectstreamflow
dc.subjectuncertainty analysis
dc.subjectUncertainty analysis
dc.titleApplication of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS