Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs

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dc.contributor.authorGhasemlounia, Redvan
dc.date.accessioned2024-06-13T20:18:14Z
dc.date.available2024-06-13T20:18:14Z
dc.date.issued2022
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractIn this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (L-jSCVW) is measured via new supplementary experimental datasets via most popular vertex angles theta (128 degrees and 60 degrees). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of L-jSCVW versus the theta, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N-st) in the SCVW, and various tailgate angles (Phi). Via dimensional analysis and pre-processing method, most effectual parameters on the L-jSCVW were obtained as Fr-1 (approaching Froude number), Re-1 (incoming Reynolds number), and y(2)/y(1) (relative sequent depths). Based on the experimental results, by increasing the values of Fr-1 and Re-1, the values of L-jSCVW noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the L-jSCVW. The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the L-jSCVW. According to the attained statistical indices (determination coefficient (R-2) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the L-jSCVW with high accuracy.
dc.identifier.doi10.1080/19648189.2021.1952112
dc.identifier.endpage6649
dc.identifier.issn1964-8189
dc.identifier.issn2116-7214
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85111156967
dc.identifier.scopusqualityQ2
dc.identifier.startpage6625
dc.identifier.urihttps://doi.org/10.1080/19648189.2021.1952112
dc.identifier.urihttps://hdl.handle.net/11501/1259
dc.identifier.volume26
dc.identifier.wosWOS:000675783200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhasemlounia, Redvan
dc.institutionauthorid0000-0003-1796-4562
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofEuropean Journal of Environmental and Civil Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectExperimental Model
dc.subjectLength of Hydraulic Jump
dc.subjectThe SCVW
dc.subjectOpen Channel Flow
dc.subjectData-Driven Methods
dc.titleUsing a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs
dc.typeArticle

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