A Multivariate Neural Network (MNN) algorithm is proposed for the reconstruction of significant wave height time series, without any increase of the error of the MNN output with the number of modelled data. The algorithm uses a weighted error function during the learning phase, to improve the modelling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangular storm model. Finally an application to the NOAA buoys moored off California shows a good performance of the MNN algorithm, both during sea storms and calm time periods.

Issue Section:
Technical Papers
Keywords:
neural nets, time series, algorithm theory, ocean waves, height measurement, storms, offshore installations, engineering computing
Topics:
Algorithms, Artificial neural networks, Buoys, Seas, Significant wave heights, Storms, Testing, Time series
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