Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90288
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dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorSangeetaen_US
dc.creatorHaji Seyed Asadollah, SBen_US
dc.creatorSharafati, Aen_US
dc.creatorSihag, Pen_US
dc.creatorAl-Ansari, Nen_US
dc.creatorChau, KWen_US
dc.date.accessioned2021-06-10T06:54:51Z-
dc.date.available2021-06-10T06:54:51Z-
dc.identifier.issn1994-2060en_US
dc.identifier.urihttp://hdl.handle.net/10397/90288-
dc.language.isoenen_US
dc.publisherHong Kong Polytechnic University, Department of Civil and Structural Engineeringen_US
dc.rights© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US]
dc.rightsThe following publication Sangeeta, Haji Seyed Asadollah, S. B., Sharafati, A., Sihag, P., Al-Ansari, N., & Chau, K. W. (2021). Machine learning model development for predicting aeration efficiency through Parshall flume. Engineering Applications of Computational Fluid Mechanics, 15(1), 889-901 is available at https://doi.org/10.1080/19942060.2021.1922314en_US]
dc.subjectAeration efficiencyen_US
dc.subjectParshall flumeen_US
dc.subjectPredictionen_US
dc.subjectMachine learning modelsen_US
dc.titleMachine learning model development for predicting aeration efficiency through Parshall flumeen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage889en_US
dc.identifier.epage901en_US
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/19942060.2021.1922314en_US
dcterms.abstractThis study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression(RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting = 0.002, R2 testing = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.-
dcterms.accessRightsopen access-
dcterms.bibliographicCitationEngineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 889-901en_US
dcterms.isPartOfEngineering applications of computational fluid mechanicsen_US
dcterms.issued2021-
dc.identifier.eissn1997-003Xen_US
dc.description.validate202106 bcvc-
dc.description.oaVersion of Record-
dc.identifier.FolderNumbera0909-n05-
dc.identifier.SubFormID2123-
dc.description.fundingSourceSelf-funded-
dc.description.pubStatusPublished-
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