Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks

Erosion and scouring caused by the outflow jets of hydraulic structures is one of the most important topics in hydraulic engineering. In free overfall spillways, a water jet impacts the erodible downstream bed almost vertically and creates a scour hole. The scour hole can affect the safety and stability of the dam. In the present paper, the multivariate adaptive regression splines (MARS) approach has been adopted as a new soft computing tool for estimating the equilibrium scour depth below free overfall spillways. Using experimental data and dimensionless parameters, the MARS model has been developed to predict scour depth. Results obtained from the MARS approach were compared with those from the artificial neural network (ANN) and decision-tree algorithm. Statistical indicators demonstrated that the MARS approach had a good performance and presented competitive results, slightly better than ANN, for the prediction of this phenomenon.