Prediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression models

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dc.authoridSammen, Saad Shauket/0000-0002-1708-0612
dc.authoridDursun, Omer Faruk/0000-0003-3923-5205
dc.authoridMalik, Anurag/0000-0002-0298-5777
dc.authorwosidSammen, Saad Shauket/F-3370-2019
dc.authorwosidSihag, Parveen/ACA-1944-2022
dc.authorwosidDursun, Omer Faruk/AAA-8464-2020
dc.authorwosidMalik, Anurag/AAF-5402-2020
dc.contributor.authorSihag, Parveen
dc.contributor.authorDursun, Omer Faruk
dc.contributor.authorSammen, Saad Shauket
dc.contributor.authorMalik, Anurag
dc.contributor.authorChauhan, Anita
dc.date.accessioned2024-08-04T20:50:38Z
dc.date.available2024-08-04T20:50:38Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, the potential of soft computing techniques namely Random Forest (RF), M5P, Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) was evaluated to predict the aeration efficiency (AE(20)) at Parshall and Modified Venturi flumes. Experiments were conducted for 26 various Modified Venturi flumes and one Parshall flume. A total of 99 observations were obtained from experiments. The results of soft computing models were compared with regression-based models (i.e., MLR: multiple linear regression, and MNLR: multiple nonlinear regression). Results of the analysis revealed that the MARS model outperformed other soft computing and regression-based models for predicting the AE(20) at Parshall and Modified Venturi flumes with Pearson's correlation coefficient (CC) = 0.9997, and 0.9992, and root mean square error (RMSE) = 0.0015, and 0.0045 during calibration and validation periods. Sensitivity analysis was also carried out by using the best executing MARS model to assess the effect of individual input variables on AE(20) of both flumes. Obtained results on sensitivity examination indicate that the oxygen deficit ratio (r) was the most effective input variable in predicting the AE(20) at Parshall and Modified Venturi flumes.en_US
dc.identifier.doi10.2166/ws.2021.161
dc.identifier.endpage4085en_US
dc.identifier.issn1606-9749
dc.identifier.issn1607-0798
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85114353162en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage4068en_US
dc.identifier.urihttps://doi.org/10.2166/ws.2021.161
dc.identifier.urihttps://hdl.handle.net/11616/100188
dc.identifier.volume21en_US
dc.identifier.wosWOS:000659491900001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIwa Publishingen_US
dc.relation.ispartofWater Supplyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectaeration efficiencyen_US
dc.subjectregression-based modelsen_US
dc.subjectsoft computing modelsen_US
dc.titlePrediction of aeration efficiency of parshall and modified venturi flumes: application of soft computing versus regression modelsen_US
dc.typeArticleen_US

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