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Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

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dc.citedby0
dc.contributor.authorNg J.L.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorChong A.H.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorSyamsunurc D.en_US
dc.contributor.authorid57192698412en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid58739268700en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid59465959500en_US
dc.date.accessioned2025-03-03T07:41:37Z
dc.date.available2025-03-03T07:41:37Z
dc.date.issued2024
dc.description.abstractMissing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input? output patterns and provide highly accurate estimation results. ? 2024 The Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/wpt.2024.265
dc.identifier.epage4354
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85211481243
dc.identifier.spage4338
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85211481243&doi=10.2166%2fwpt.2024.265&partnerID=40&md5=bd2f334a415c93741f0c8a3e15ba9ee4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36220
dc.identifier.volume19
dc.pagecount16
dc.publisherIWA Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleWater Practice and Technology
dc.subjectFuzzy neural networks
dc.subjectMean square error
dc.subjectArtificial intelligence methods
dc.subjectArtificial neural network
dc.subjectArtificial neuro-fuzzy inference system
dc.subjectAuto regressive integrated moving average
dc.subjectAutoregressive integrated moving average(ARIMA)
dc.subjectData estimation
dc.subjectMalaysia
dc.subjectMissing streamflow data estimation
dc.subjectNeural-networks
dc.subjectNeuro-fuzzy inference systems
dc.titleEstimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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