Recurrent neural networks with Bayesian Regularization algorithm provide better prediction of significant wave height compared to other tested algorithms.
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May enhance wave height forecasts in coastal models; leaves open prospective validation before operational use.
In meteorological and marine engineering applications prediction of Significant Wave Height (SWH) plays a major role for forecasting cyclones, earthquakes & tsunamis that may occur in the ocean and warn the society for appropriate action. Recently, researchers are exploring the use of soft computing techniques to predict SWH In this work, the wind and wave data obtained from moored buoys of Bay of Bengal is used to predict the SWH using Artificial Neural Networks (ANN). The Recurrent and Feed Forward Networks were analyzed using Levenberg Marquardt (LM), Conjugate Gradient (CG) and Bayesian Regularization (BR) algorithms. Results indicate that Recurrent Networks with BR algorithm has higher correlation and less error.
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Abhigna et al. (2017) studied this question.