Mathematical expression for discharge coefficient of Weir-Gate using soft computing techniques

This research aimed to develop artificial intelligence models, including multivariate adaptive regression splines (MARS), group of method data handling (GMDH), and genetic expression programming(GEP) techniques, in order to estimate the discharge coefficient (Cd) of the Weir-Gate structure. The parameters involved in this problem are the ratios of weir height to the gated opening (y/d), the weir width to the gated opening (b/d), contraction coefficient (b/B) and the upstream head to the gated opening (H/d). Results of applied methods declared that all of them have acceptable performance for modeling and predicting theCd. However, the MARS model outperforms the other models. The error indices of MARS model in training stage were (R-2 = 0.95, RMSE = 0.007) and in the testing were (R-2 = 0.89, RMSE = 0.012). Reviewing the prepared models indicated thaty/dandH/dare the impressive factors on theCdof Weir-Gate.