The Reconstruction of Significant Wave Height Time Series by Using a Neural Network Approach.

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 number of modeled data. The algorithm uses a weight error function during the learning phase, to improve the modeling of the higher significant wave height. The ability of the MNN to reconstruct sea storms is tested by applying the equivalent triangle 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. [ABSTRACT FROM AUTHOR]