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.
The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach
Contributed by the OOAE Division for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received November 2, 2002; final revision, February 6, 2004. Associate Editor: B. Leira.
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Arena, F., and Puca, S. (February 6, 2004). "The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach ." ASME. J. Offshore Mech. Arct. Eng. August 2004; 126(3): 213–219. https://doi.org/10.1115/1.1782646
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