Correlating Wave Hindcast and Buoy data with Artificial Neural Networks

Abstract

This work presents results from the use of Artificial Neural Networks (ANN) to improve wave models hindcasting capacity off the South coast of Portugal. Comparison of the original model results with field measurements showed significant non linear deviations. To compensate for such deviations, a three-layer Multilayer Perceptron (MLP – a type of an ANN) was trained, using the Levenberg-Marquardt method, to improve the fit between the hindcast (generated by WW3) and Faro buoy data in an effort to reconstruct missing data from the wave buoy time series. The results obtained so far are very positive; with the training with annual datasets showing better results than the training with the entire dataset, while both improved significantly the fitting of the raw model results. Further improvements are expected by trying different ANN types, by searching for optimised ANN input-output structure, and by performing sub-set selection on the data sets.