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Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models

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dc.citedby17
dc.contributor.authorEbtehaj I.en_US
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorMalik A.en_US
dc.contributor.authorSihag P.en_US
dc.contributor.authorAl-Janabi A.M.S.en_US
dc.contributor.authorChau K.-W.en_US
dc.contributor.authorBonakdari H.en_US
dc.contributor.authorid55826666000en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid56486779100en_US
dc.contributor.authorid57195985799en_US
dc.contributor.authorid57205418996en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid23388736200en_US
dc.date.accessioned2023-05-29T09:11:13Z
dc.date.available2023-05-29T09:11:13Z
dc.date.issued2021
dc.description.abstractAccurate prediction of water level (WL) is essential for the optimal management of different water resource projects. The development of a reliable model for WL prediction remains a challenging task in water resources management. In this study, novel hybrid models, namely, Generalized Structure-Group Method of Data Handling (GS-GMDH) and Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM) were proposed to predict the daily WL at Telom and Bertam stations located in Cameron Highlands of Malaysia. Different percentage ratio for data division i.e. 50%�50% (scenario-1), 60%�40% (scenario-2), and 70%�30% (scenario-3) were adopted for training and testing of these models. To show the efficiency of the proposed hybrid models, their results were compared with the standalone models that include the Gene Expression Programming (GEP) and Group Method of Data Handling (GMDH). The results of the investigation revealed that the hybrid GS-GMDH and ANFIS-FCM models outperformed the standalone GEP and GMDH models for the prediction of daily WL at both study sites. In addition, the results indicate the best performance for WL prediction was obtained in scenario-3 (70%�30%). In summary, the results highlight the better suitability and supremacy of the proposed hybrid GS-GMDH and ANFIS-FCM models in daily WL prediction, and can, serve as robust and reliable predictive tools for the study region. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2021.1966837
dc.identifier.epage1361
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85115277381
dc.identifier.spage1343
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85115277381&doi=10.1080%2f19942060.2021.1966837&partnerID=40&md5=851cd5f7fc063e3fb8fa5d6e83241ea1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26496
dc.identifier.volume15
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titlePrediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM modelsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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