Predictive modeling the side weir discharge coefficient using neural network

Weirs are common structures that are widely used in the almost water engineering projects such as hydropower systems, irrigation and drainage networks along with sewage networks. A side weir has many possible uses in hydraulic engineering and has been investigated as an important structure in hydro systems, as well. In this paper, predicting the discharge coefficient of side weirs ($$Cd_{sw}$$Cdsw) was considered using the empirical formulas, multilayer perceptron (MLP) and radial basis function (RBF) neural network as delegate of artificial neural network models. The results indicate that Emiroglu formula by correlation coefficient of (R2 = 0.65) and root mean square error of (RMSE = 0.03) is the accurate one among the empirical formulas. Evaluating the performance of the RBF model with ten neurons in the hidden layer involving error indices of (R2 = 0.71 and RMSE = 0.08) showed that this model was a bit better than Emiroglu formula. The structure of MLP model was considered as similar to RBF model whereas the tangent sigmoid was used instead to the radial basis function. The results of MLP model showed that this model with R2 = 0.89 and RMSE = 0.067 had suitable performance for predicting discharge coefficient. Performance of MLP was more accurate compared to RBF model.