Prediction of Wave Breaking on a Gravel Beach by an Artificial Neural Network

Abstract

Abstract A great many studies on wave breaking have been carried out, and much experimental and field data have been documented. Moreover, on the basis of various data sets, many empirical formulas based primarily on regression analysis have been proposed to quantitatively estimate wave breaking for engineering applications. However, wave breaking has an inherent variability, which suggests that a linear statistical approach such as regression analysis might be inadequate. This study presents an alternative nonlinear method using an artificial neural network (ANN), one of the soft computing methods, for predicting breaking-wave heights and water depths. Using data from laboratory experiments showing that wave breaking characteristics on a gravel beach are different from those on a sandy beach, we developed a three-layered feed-forward type of network to obtain the output of wave-breaking heights and water depths using deepwater heights, wave periods, and seabed conditions as inputs. In particular, the eff...