Applicability of several soft computing approaches in modeling oxygen transfer efficiency at baffled chutes

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dc.authorscopusid26638786000
dc.authorscopusid6507051085
dc.authorscopusid56689904500
dc.authorscopusid6603895695
dc.contributor.authorGerger R.
dc.contributor.authorKisi O.
dc.contributor.authorDursun O.F.
dc.contributor.authorEmiroglu M.E.
dc.date.accessioned2024-08-04T19:59:31Z
dc.date.available2024-08-04T19:59:31Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe present study investigates the accuracy of five different data-driven techniques in estimating oxygen transfer efficiency in baffled chutes: feedforward neural network (FFNN), radial basis neural network (RBNN), generalized regression neural network (GRNN), adaptive neuro fuzzy inference system with subtractive clustering (ANFIS-SC), and adaptive neuro fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM). Baffled apron chutes or drops are used on channel structures to dissipate the energy in the flow. A baffled chute design is effective both in energy dissipation and in aerating the flow and reducing nitrogen supersaturation. There is a close relationship between energy dissipation and oxygen transfer efficiency. This study aims to determine the aeration efficiency of baffled chutes with stepped (S), wedge (W), trapezoidal (T), and T-shaped (T-S) baffle blocks. The performances of the FFNN, RBNN, GRNN, ANFIS-SC, and ANFIS-FCM models are compared with those of multilinear and nonlinear regression models. Based on the comparisons, it was observed that all data-driven models could be successfully employed in modeling the aeration efficiency of S, W, and T-S baffle blocks from the available experimental data. Among data-driven models, the FFNN model was found to be the best. © 2016 American Society of Civil Engineers.en_US
dc.identifier.doi10.1061/(ASCE)IR.1943-4774.0001153
dc.identifier.issn0733-9437
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85016809358en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1061/(ASCE)IR.1943-4774.0001153
dc.identifier.urihttps://hdl.handle.net/11616/90692
dc.identifier.volume143en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineers (ASCE)en_US
dc.relation.ispartofJournal of Irrigation and Drainage Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAeration efficiencyen_US
dc.subjectBaffled chuteen_US
dc.subjectData-driven modelingen_US
dc.subjectDissolved oxygenen_US
dc.subjectEnergy dissipationen_US
dc.subjectEnvironmental hydraulicsen_US
dc.subjectOxygen transferen_US
dc.titleApplicability of several soft computing approaches in modeling oxygen transfer efficiency at baffled chutesen_US
dc.typeArticleen_US

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