Spur dikes are hydraulic structures that are constructed within rivers with the purpose of mitigating bank erosion, enhancing the stability of riverbanks. It has received considerable attention and being adopted throughout the world. But scour around spur dikes affects their hydraulic performance and stability. Therefore, a precise prediction of scour depth fluctuations is essential for the development of reliable and cost-effective designs of spur dikes. In this study, two type of machine learning techniques, say regression (linear regression and non-linear regression) and explicit expression (multivariate adaptive regression splines, M5P tree and group method of data handling) based machine learning techniques, are applied to predict scour depth around spur dikes. For this purpose, a database of 154 experimental observations has been collected from existing literature, of which 80% and 20% observations have been used for training and testing subsets, respectively. The root mean square error (RMSE), coefficient of determination (R-Square), and coefficient of correlation (CC), and mean absolute error (MAE) have been used to compare the performance and accuracy of these techniques. The multivariate adaptive regression splines (MARS) technique has shown the highest accuracy (RMSE=0.0883, R-Square=0.9869, CC=0.9934, and MAE=0.0485) and is the best machine learning technique for predicting scour depth around spur dikes as compared to M5P tree (M5PT), group method of data handling (GMDH), non-linear regression (NLR) and linear regression (LR). In addition, two regression-based equations and three explicit expression-based equations are derived for the prediction of scour depth at any instances.
