Length prediction of non-aerated region flow at baffled chutes using intelligent nonlinear regression methods

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridALCIN, Omer/0000-0002-2917-3736
dc.authoridDursun, Omer Faruk/0000-0003-3923-5205;
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidALCIN, Omer/AAH-3525-2020
dc.authorwosidDursun, Omer Faruk/AAA-8464-2020
dc.authorwosidKaya, Nihat/V-9067-2018
dc.contributor.authorDursun, O. Faruk
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorKaya, Nihat
dc.contributor.authorAlcin, O. Faruk
dc.date.accessioned2024-08-04T20:41:39Z
dc.date.available2024-08-04T20:41:39Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBaffled chutes are used in irrigation systems, storm water systems, wastewater canal chutes, river training, and drop structures for energy dissipation. Two flow regions occur on the flow surface of baffled chutes. These are black water and white water regions. Knowing the location of the inception point where white water begins to appear on the surface is important for determination of the non-aerated flow region. Thus, cavitation damage can be prevented. In this study, 160 laboratory test results have been used for determining black water length (i.e., length of the non-aerated region) of baffled chutes with stepped, wedge, trapezoidal, and T-shaped baffle blocks. The obtained observation data have been analyzed by well-known soft computing methods such as artificial neural networks (ANN), curve fitting (CF), non-linear regression (NLR) and special extreme learning machine (ELM). The methods' performance in mapping input data to the output were compared. The mean regression errors calculated by the curve fitting model, ANN, NLR and ELM are obtained as 2.5, 8.0, 11.25 and 0.8 %, respectively. The experimental results show that ELM's nonlinear system modeling capability is superior to ANN, NLR, and CF.en_US
dc.identifier.doi10.1007/s12665-016-5486-8
dc.identifier.issn1866-6280
dc.identifier.issn1866-6299
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-84963705896en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s12665-016-5486-8
dc.identifier.urihttps://hdl.handle.net/11616/97268
dc.identifier.volume75en_US
dc.identifier.wosWOS:000375063400051en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Earth Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBaffled chuteen_US
dc.subjectBlack water distanceen_US
dc.subjectEnergy dissipationen_US
dc.subjectELMen_US
dc.subjectANNen_US
dc.subjectNLRen_US
dc.subjectCurve fitting modelen_US
dc.titleLength prediction of non-aerated region flow at baffled chutes using intelligent nonlinear regression methodsen_US
dc.typeArticleen_US

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