Evaluating Discharge Coefficient of Rectangular Sharp Crested Weirs Using Machine Learning Models.
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| Title: | Evaluating Discharge Coefficient of Rectangular Sharp Crested Weirs Using Machine Learning Models. |
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| Authors: | Dhar, Mrinmoy1 (AUTHOR) mrinmoydhar@iutripura.edu.in, Sapkota, Sanjog Chhetri2 (AUTHOR) sanjogchhetrisapkota@gmail.com, Saha, Prasenjit3 (AUTHOR) prasenjitsaha@iutripura.edu.in, Arora, Sameer4 (AUTHOR) sameer_arora01@yahoo.co.in |
| Source: | Water Resources Management. Jun2025, Vol. 39 Issue 8, p4151-4171. 21p. |
| Subject Terms: | *Machine learning, *Discharge coefficient, *Statistical models, *Standard deviations, *Support vector machines |
| Abstract: | In the realm of flow measurement using rectangular sharp-crested weirs, the stage-discharge relationship, primarily represented by the discharge coefficient (Cd), holds paramount significance. In order to comprehend and predict the behavior of Cd effectively, six machine learning (ML) algorithms, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaboost (ADB), Xgboost (XGB) and Catboost (CATB), are assessed. The performances of each of these approaches have been evaluated based on statistical criterions and using Taylor diagram. It is revealed that all six techniques can predict the discharge coefficient with excellent accuracy, with the Catboost model outperforming the other models. In the prediction of Cd, the values of the coefficient of determination and root mean square error obtained from the CATB model are 0.9782 and 0.0064, respectively. The extensive nonlinear behaviour exhibited by the ML models is also addressed using shapely additives explanation (SHAP) framework. Further, a comparison of the predictions of ML approaches with the results of existing empirical formulas has been made. The accuracy of ML approaches is found to be much higher than that of either of the existing empirical formulas, indicating the superior prediction capability of ML models. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 186289349 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating Discharge Coefficient of Rectangular Sharp Crested Weirs Using Machine Learning Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dhar%2C+Mrinmoy%22">Dhar, Mrinmoy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mrinmoydhar@iutripura.edu.in</i><br /><searchLink fieldCode="AR" term="%22Sapkota%2C+Sanjog+Chhetri%22">Sapkota, Sanjog Chhetri</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sanjogchhetrisapkota@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Saha%2C+Prasenjit%22">Saha, Prasenjit</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> prasenjitsaha@iutripura.edu.in</i><br /><searchLink fieldCode="AR" term="%22Arora%2C+Sameer%22">Arora, Sameer</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> sameer_arora01@yahoo.co.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Water+Resources+Management%22">Water Resources Management</searchLink>. Jun2025, Vol. 39 Issue 8, p4151-4171. 21p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Discharge+coefficient%22">Discharge coefficient</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br />*<searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the realm of flow measurement using rectangular sharp-crested weirs, the stage-discharge relationship, primarily represented by the discharge coefficient (Cd), holds paramount significance. In order to comprehend and predict the behavior of Cd effectively, six machine learning (ML) algorithms, namely, Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaboost (ADB), Xgboost (XGB) and Catboost (CATB), are assessed. The performances of each of these approaches have been evaluated based on statistical criterions and using Taylor diagram. It is revealed that all six techniques can predict the discharge coefficient with excellent accuracy, with the Catboost model outperforming the other models. In the prediction of Cd, the values of the coefficient of determination and root mean square error obtained from the CATB model are 0.9782 and 0.0064, respectively. The extensive nonlinear behaviour exhibited by the ML models is also addressed using shapely additives explanation (SHAP) framework. Further, a comparison of the predictions of ML approaches with the results of existing empirical formulas has been made. The accuracy of ML approaches is found to be much higher than that of either of the existing empirical formulas, indicating the superior prediction capability of ML models. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=186289349 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11269-025-04152-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 4151 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Discharge coefficient Type: general – SubjectFull: Statistical models Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Evaluating Discharge Coefficient of Rectangular Sharp Crested Weirs Using Machine Learning Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dhar, Mrinmoy – PersonEntity: Name: NameFull: Sapkota, Sanjog Chhetri – PersonEntity: Name: NameFull: Saha, Prasenjit – PersonEntity: Name: NameFull: Arora, Sameer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09204741 Numbering: – Type: volume Value: 39 – Type: issue Value: 8 Titles: – TitleFull: Water Resources Management Type: main |
| ResultId | 1 |
