Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90287
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dc.contributorDepartment of Civil and Environmental Engineeringen_US
dc.creatorSharafati, Aen_US
dc.creatorHaghbin, Men_US
dc.creatorTiwari, NKen_US
dc.creatorBhagat, SKen_US
dc.creatorAl-Ansari, Nen_US
dc.creatorChau, KWen_US
dc.creatorYaseen, ZMen_US
dc.date.accessioned2021-06-10T06:54:51Z-
dc.date.available2021-06-10T06:54:51Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/90287-
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.rights© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Sharafati, A., Haghbin, M., Tiwari, N. K., Bhagat, S. K., Al-Ansari, N., Chau, K. W., & Yaseen, Z. M. (2021). Performance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy models. Engineering Applications of Computational Fluid Mechanics, 15(1), 627-643 is available at https://doi.org/10.1080/19942060.2021.1893224en_US
dc.subjectAdaptive neuro-fuzzy inference systemsen_US
dc.subjectHybrid modelen_US
dc.subjectMetaheuristic modelsen_US
dc.subjectSediment ejectoren_US
dc.subjectSediment removal efficiencyen_US
dc.titlePerformance evaluation of sediment ejector efficiency using hybrid neuro-fuzzy modelsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage627en_US
dc.identifier.epage643en_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2021.1893224en_US
dcterms.abstractSediment transport in the ejector is highly stochastic and non-linear in nature, and its accurate estimation is a complex and challenging mission. This study attempts to investigate the sediment removal estimation of sediment ejector using newly developed hybrid data-intelligence models. The proposed models are based on the hybridization of adaptive neuro-fuzzy inference systems (ANFIS) with different metaheuristic algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and ant colony optimization (ACO). The proposed models are constructed with various related input variables such as sediment concentration, flow depth, velocity, sediment size, Froude number, extraction ratio, number of tunnels and sub-tunnels, and flow depth at upstream of the sediment ejector. The estimation capacity of the developed hybrid models is assessed using several statistical evaluation indices. The modeling results obtained for the studied ejector sediment removal estimation demonstrated an optimistic finding. Among the developed hybrid models, ANFIS-PSO model exhibited the best predictability potential with maximum correlation coefficient values CC Train = 0.915 and CCTest = 0.916.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 627-643en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2021-
dc.identifier.scopus2-s2.0-85103859910-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202106 bcvcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0909-n04-
dc.identifier.SubFormID2122-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
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