Modeling the discharge coefficient of labyrinth sluice gates using hybrid support vector regression and metaheuristic algorithms DOI Creative Commons
Aliasghar Azma, Alistair G.L. Borthwick, Reza Ahmadian

и другие.

Physics of Fluids, Год журнала: 2025, Номер 37(4)

Опубликована: Апрель 1, 2025

Gates and weirs are frequently used hydraulic structures employed for controlling water flow rates in irrigation drainage networks. Therefore, accurately estimating the discharge coefficient (Cd) is important precise measurement. The present study intelligent predictive models modeling Cd labyrinth sluice gates. For this purpose, key dimensionless parameters reliable experimental datasets were used. support vector regression (SVR) model was hybridized with particle swarm optimization (PSO) genetic algorithms (GA). statistical metrics graphical plots evaluated performance of generated models. Three commonly indicators, namely root mean square error (RMSE), absolute (MAE), determination (R2), quantitatively evaluating proposed SVR-PSO achieved lowest values RMSE (0.0287) MAE (0.0209) highest value R2 (0.9732), indicating that it more accurate than SVR-GA (RMSE = 0.0324, 0.0257, 0.9685) SVR 0.0575, 0.0468, 0.8958) on testing data. findings revealed hybrid methods standalone model. In addition, regarding objective function criterion (OBF), (OBF 0.0245) 0.0273) had lower OBF provided estimates compared to existing nonlinear regression-based formulas data-driven approaches. Finally, sensitivity SHapley Additive exPlanations (SHAP) analyses determined relative importance each input variable prediction Cd.

Язык: Английский

Machine Learning-Based Modeling of Discharge Coefficients in Labyrinth Sluice Gates DOI

Thaer Hashem,

Ahmed Y. Mohammed, Ali Sharifi

и другие.

Flow Measurement and Instrumentation, Год журнала: 2025, Номер unknown, С. 102823 - 102823

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Modeling the discharge coefficient of labyrinth sluice gates using hybrid support vector regression and metaheuristic algorithms DOI Creative Commons
Aliasghar Azma, Alistair G.L. Borthwick, Reza Ahmadian

и другие.

Physics of Fluids, Год журнала: 2025, Номер 37(4)

Опубликована: Апрель 1, 2025

Gates and weirs are frequently used hydraulic structures employed for controlling water flow rates in irrigation drainage networks. Therefore, accurately estimating the discharge coefficient (Cd) is important precise measurement. The present study intelligent predictive models modeling Cd labyrinth sluice gates. For this purpose, key dimensionless parameters reliable experimental datasets were used. support vector regression (SVR) model was hybridized with particle swarm optimization (PSO) genetic algorithms (GA). statistical metrics graphical plots evaluated performance of generated models. Three commonly indicators, namely root mean square error (RMSE), absolute (MAE), determination (R2), quantitatively evaluating proposed SVR-PSO achieved lowest values RMSE (0.0287) MAE (0.0209) highest value R2 (0.9732), indicating that it more accurate than SVR-GA (RMSE = 0.0324, 0.0257, 0.9685) SVR 0.0575, 0.0468, 0.8958) on testing data. findings revealed hybrid methods standalone model. In addition, regarding objective function criterion (OBF), (OBF 0.0245) 0.0273) had lower OBF provided estimates compared to existing nonlinear regression-based formulas data-driven approaches. Finally, sensitivity SHapley Additive exPlanations (SHAP) analyses determined relative importance each input variable prediction Cd.

Язык: Английский

Процитировано

0