Soft computing-based model development for estimating the aeration efficiency through Parshall flume and Venturi flumes

Basit Öğe Kaydı
dc.authoridDursun, Omer Faruk/0000-0003-3923-5205
dc.authoridThakur, Mohindra/0000-0003-1270-0963
dc.authorwosidSihag, Parveen/ACA-1944-2022
dc.authorwosidDursun, Omer Faruk/AAA-8464-2020
dc.authorwosidThakur, Mohindra/HNJ-3008-2023
dc.contributor.authorPuri, Diksha
dc.contributor.authorSihag, Parveen
dc.contributor.authorSadeghifar, Tayeb
dc.contributor.authorDursun, Omer Faruk
dc.contributor.authorThakur, Mohindra Singh
dc.date.accessioned2024-08-04T20:53:35Z
dc.date.available2024-08-04T20:53:35Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study compares the efficacy of soft computing techniques namely, Random Forest, M5P tree and Adaptive Neuro Fuzzy Inference System to predict the aeration efficiency through a combined dataset of Parshall and modified Venturi Flumes. For the development and validation of the model, in all, 99 experimental observations were used. The model's development and validation were done by utilizing six independent variables, discharge, throat width, throat length, sill height, oxygen deficit ratio and the exponent factor as inputs whereas aeration efficiency was considered as a target. The performance of developed models is measured using six different goodness of fit parameters which are correlation coefficient, coefficient of determination, mean absolute error, mean squared error, root mean square error and mean absolute percentage error. Outcomes of the present analysis revealed that all developed models are capable of handling prediction due to their higher correlation coefficient (CC) values. However, Random Forest model outperformed other soft computing-based models for estimating the aeration efficiency with a correlation coefficient of 0.9981, mean absolute error value of 0.0023, and a mean squared error being 0.00 in the testing stage. Further, results obtained from sensitivity investigation indicate that the oxygen deficit ratio which contains the elements of saturated oxygen concentration, upstream oxygen concentration, and downstream oxygen concentration is the most effective input variable for estimating the aeration efficiency using this data set. Since oxygen deficit is highly sensitive to aeration efficiency, the values of saturated oxygen concentration, upstream and downstream oxygen concentration require due consideration.en_US
dc.identifier.doi10.1007/s41939-023-00153-0
dc.identifier.endpage413en_US
dc.identifier.issn2520-8160
dc.identifier.issn2520-8179
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85151920706en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage401en_US
dc.identifier.urihttps://doi.org/10.1007/s41939-023-00153-0
dc.identifier.urihttps://hdl.handle.net/11616/101277
dc.identifier.volume6en_US
dc.identifier.wosWOS:000967294800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofMultiscale and Multidisciplinary Modeling Experiments and Designen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParshall flumeen_US
dc.subjectModified Venturi flumesen_US
dc.subjectAdaptive neuro fuzzy inference systemen_US
dc.subjectM5Pen_US
dc.subjectRandom foresten_US
dc.titleSoft computing-based model development for estimating the aeration efficiency through Parshall flume and Venturi flumesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu