On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques
Prediction of flow discharge coefficient, Cd, for a sluice gate under free and submerged flow conditions is one of the essential issues in hydraulics. In recent years, various semi-empirical equations have been developed in order to predict Cd for a sluice gate that application of those formulas und...
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Semnan University
2022-12-01
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| Series: | مجله مدل سازی در مهندسی |
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| Online Access: | https://modelling.semnan.ac.ir/article_6160_fe8296f0aa8778d87fd75da744ec825a.pdf |
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| author | arman alirezazadeh sadaghiyani Mirali Mohammadi misagh galvani babak vaheddoost |
| author_facet | arman alirezazadeh sadaghiyani Mirali Mohammadi misagh galvani babak vaheddoost |
| author_sort | arman alirezazadeh sadaghiyani |
| collection | DOAJ |
| description | Prediction of flow discharge coefficient, Cd, for a sluice gate under free and submerged flow conditions is one of the essential issues in hydraulics. In recent years, various semi-empirical equations have been developed in order to predict Cd for a sluice gate that application of those formulas under submerged flow conditions suffered from large errors. The aim of the present research is to use Gaussian Process Regression (GPR) and Support Vector Machine (SVM) used in soft computing techniques, so that estimating Cd in submerged flow conditions and comparing the results with quasi-experimental methods are of interest, herein. For this purpose, an experimental dataset comprised of 122 data points were used to feed the methods utilized. Different combinations of dimensionless parameters were then prepared and the performance of the afore mentioned methods were assessed. The results showed that SVM with input parameters of 𝑦𝑡⁄𝑤, 𝑦0⁄𝑤, 1/𝐹𝑟2 and S by the values of Root Mean Square Error (RMSE=0.017), correlation coefficient (R=0.97) and Nash-Sutcliffe Equivalent (NSE=0.95) had a better performance than GPR and other semi-empirical approaches, indeed. |
| first_indexed | 2024-03-07T22:05:14Z |
| format | Article |
| id | doaj.art-6507f7968dde4654b00e12bd4ba309f9 |
| institution | Directory Open Access Journal |
| issn | 2008-4854 2783-2538 |
| language | fas |
| last_indexed | 2024-03-07T22:05:14Z |
| publishDate | 2022-12-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj.art-6507f7968dde4654b00e12bd4ba309f92024-02-23T19:10:14ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382022-12-01207111210.22075/jme.2022.23349.20906160On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniquesarman alirezazadeh sadaghiyani0Mirali Mohammadi1misagh galvani2babak vaheddoost3Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, IranAssociate Professor in Civil Eng. Hydraulics & River Eng. Mechanics, Department of Civil Engineering, Faculty of Eng., Urmia University.MSc Student, Department of Civil Eng., Faculty of Eng., Urmia University, Urmia, Iran.Assistant Professor, Department of Civil Eng., Faculty of Eng., Bursa Technical University, Bursa, Turkey.Prediction of flow discharge coefficient, Cd, for a sluice gate under free and submerged flow conditions is one of the essential issues in hydraulics. In recent years, various semi-empirical equations have been developed in order to predict Cd for a sluice gate that application of those formulas under submerged flow conditions suffered from large errors. The aim of the present research is to use Gaussian Process Regression (GPR) and Support Vector Machine (SVM) used in soft computing techniques, so that estimating Cd in submerged flow conditions and comparing the results with quasi-experimental methods are of interest, herein. For this purpose, an experimental dataset comprised of 122 data points were used to feed the methods utilized. Different combinations of dimensionless parameters were then prepared and the performance of the afore mentioned methods were assessed. The results showed that SVM with input parameters of 𝑦𝑡⁄𝑤, 𝑦0⁄𝑤, 1/𝐹𝑟2 and S by the values of Root Mean Square Error (RMSE=0.017), correlation coefficient (R=0.97) and Nash-Sutcliffe Equivalent (NSE=0.95) had a better performance than GPR and other semi-empirical approaches, indeed.https://modelling.semnan.ac.ir/article_6160_fe8296f0aa8778d87fd75da744ec825a.pdfsluice gate"discharge coefficient"artificial intelligence"support vector machine (svm)""semi-emperical methods" |
| spellingShingle | arman alirezazadeh sadaghiyani Mirali Mohammadi misagh galvani babak vaheddoost On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques مجله مدل سازی در مهندسی sluice gate" discharge coefficient" artificial intelligence" support vector machine (svm)" " semi-emperical methods" |
| title | On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques |
| title_full | On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques |
| title_fullStr | On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques |
| title_full_unstemmed | On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques |
| title_short | On the Prediction of Discharge Coefficient for Sluice Gates under Submerged Flow Conditions using Soft Computing Techniques |
| title_sort | on the prediction of discharge coefficient for sluice gates under submerged flow conditions using soft computing techniques |
| topic | sluice gate" discharge coefficient" artificial intelligence" support vector machine (svm)" " semi-emperical methods" |
| url | https://modelling.semnan.ac.ir/article_6160_fe8296f0aa8778d87fd75da744ec825a.pdf |
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