Prediction of current-induced scour depth around pile groups using MARS, CART, and ANN approaches

Abstract

Scour around piles is considered as one of the most important phenomena which could undermine the stability of piles. Data-driven methods are now being used more and more to predict complex phenomena such as scour around of pile groups. In this study, three data-driven methods including multivariate adaptive regression spline (MARS), classification and regression tree (CART) and artificial neural network (ANN), are used for determining current-induced scour depth around pile groups in clear water conditions. Obtained results are compared with those of M5 Model Tree (M5MT) which shows the higher precision of them. The calculated statistical indices revealed the higher accuracy of MARS in prediction of scour depth (correlation coefficient [CC] = 0.872 and root mean square error [RMSE] = 0.233) in comparison with ANN (CC = 0.843 and RMSE = 0.256), CART (CC = 0.734 and RMSE = 0.323) and M5MT (CC = 0.761 and RMSE = 0.404) for testing data. Furthermore, the developed discrepancy ratio (DDR) index confirms the outperformance of MARS in comparison with other approaches for prediction of scour depth. Finally, the parametric investigations conducted through MARS show that the parameter of has the highest effect on the scour depth. This finding is in agreement with the results of CART.