Predicting scour depth at seawalls using GP and ANNs

Abstract

Accurate prediction of maximum scour depth is important for the optimum design of seawall structure. Owing to the complex interaction of the incident waves, sediment bed, and seawalls, the prediction of the scour depth is not an easy task to accomplish. Undermining the recent experimental and numerical advancement, the available empirical equations have limited accuracy and applicability. The aim of this study is to investigate the application of robust data-mining methods including genetic programming (GP) and artificial neural networks (ANNs) for predicting the maximum scour depth at seawalls under the broken and breaking waves action. The performance of GP and ANNs models has been compared with the existing empirical formulas employing statistical measures. The results indicated that both the GP and ANNs models functioned significantly better than the existing empirical formulas. Furthermore, the capability of GP was used to produce meaningful mathematical rules, and an analytical formula for predicting the maximum scour depth at seawalls under breaking and broken waves' attacks was developed by utilizing GP.