Prediction of discharge coefficient of the trapezoidal broad-crested weir flow using soft computing techniques

Weirs are hydraulic structures mostly used to measure the flow discharge and control the flow level in artificial or natural open channels. The ratio of the actual discharge to the theoretical discharge (discharge coefficient-C-d) must be known, in order to calculate the discharge of the channel having the weir. In this study, 91 experimental measurements are taken on seven trapezoidal broad-crested weirs with different upstream and downstream slopes. Experimentally measured flow properties are used to validate numerical models based on the computational fluid dynamics (CFD) methods. Two new weir geometries, not experimentally measured, are added in the numerical modeling, and 270 C-d values are calculated for nine weir geometries using numerical modeling. Theoretical C-d values are estimated using the artificial neural network (ANN), support vector machine (SVM), and M5Tree methods. In the models, the Froude number in the upstream region and dimensionless parameters of the flow are used as inputs. The performance of these methods has been examined to estimate the C-d values for eight cases. The performances of the methods are evaluated by the coefficient of determination (R-2), root-mean-square error, mean absolute percentage error, and Nash-Sutcliffe model efficiency coefficient. The study results show that the Froude number significantly increases the performance of the models in estimating C-d values, and the ANN method is more successful in determining C-d than other methods.