Day ahead ocean swell forecasting with recursively regularized recurrent neural networks

Day ahead forecasts of ocean swell amplitude at fixed deep water observation platforms could provide critical decision making information for a large number of costal ocean activities. Currently the hourly measurements of wave height data provided from fixed deep water observation platforms tend to be irregular, and contaminated with noise. This data quality issue has been problematic for previous approaches to wave amplitude forecasting. This paper proposes a solution to the data quality issue through recursively regularized weight estimation for a recurrent Multi-layer Perceptron neural network. Experimentation has shown that the proposed model out preforms standard feed forward models as well as extended Kalman filter trained recurrent neural models in a next day forecasting task.