Modeling discharge coefficient of radial gates under submerged conditions using kernel-based approaches

Prediction of flow discharge coefficient of gates is one of the essential issues in water engineering sciences. In recent years, various semi-empirical equations have been developed in order to predict the discharge coefficient of radial gates that the application of these formulas under submerged flow conditions suffered from large errors. The aim of present study is to apply robust Gaussian Process Regression (GPR) and Support Vector Machine (SVM) to predict discharge coefficient of radial gates under submerged flow conditions and compare the obtained results with well-known semi-empirical approaches. For this purpose, an extensive experimental dataset comprises 2136 data points were used to feed the utilized methods. Different combinations of dimensionless parameters were prepared and the performance of aforementioned methods were assessed. The obtained results showed that GPR method with input parameters of y0-yt/w and yt/w yields a correlation coefficient (R) of 0.983, a Nash- Sutcliffe efficiency (NSE) of 0.967 and root mean squared error (RMSE) of 0.027 and indicated superior performance compared with employed SVM and other semi-empirical approaches.