Application of nested artificial neural network for the prediction of significant wave height

Abstract

Significant wave height is the most important parameter in feasibility studies and the design of wave energy converters. In this study, the novel nested artificial neural networks were developed and applied to predict significant wave height at twenty selected locations of the North Sea. A nested artificial neural network applies nonlinear machine learning models as transfer functions in the neurons of networks. Two input parameters comprising wind speed and wind direction were implemented to train the derived models. The results show that the derived nonlinear machine learning models are about 18.39% more accurate than the linear regression technique. The statistical indices confirm that the nested artificial neural network may increases the accuracy of traditional models by up to 34%. Among all applied models, the nested artificial neural network developed based on the integration of particle swarm optimization algorithm and adaptive neuro-fuzzy inference system, with RMSE = 0.525m and R2 = 0.84, provides the most accurate prediction of wave heights. The high accuracy of the results indicates that if computational time is not a very critical factor for users, then the application of nested artificial neural networks may be recommended for the modeling of wave parameters and other complex problems.