Artificial neural networks in merging wind wave forecasts with field observations

Abstract

An attempt to improve wind wave short-term forecasts based on artificial neural networks is reported. The novelty of the study consists in the use of relatively short time series of wave observations collected over 2 consecutive months to accomplish the tasks of wave predictions and data assimilation. Separate neural networks were developed to predict five wind wave parameters, namely, the significant wave height, zero-up-crossing wave period, peak wave period, mean direction at the peak period and directional spreading over intervals of 3, 6, 12 and 24 hours, and to correct these predictions. Data from a directional buoy were used to train and validate the networks. The results of the simulations carried out without and with the proposed methodology were favourably compared to time series of wave parameters estimated in the field. Moreover, time series plots and scatterplots of the wave characteristics as well as statistics show an improvement of the results achieved due to data merging.