Prediction of Wave Energy Spectrum Based on Ship Motions Using a Data-Driven Approach

Wave energy spectra are used to create sea states and to obtain ship motion transfer functions for different frequencies. These transfer functions are non-linear. Hence, the precise estimation is not straightforward. In this study, the spectral parameters, significant wave height and peak period, are obtained via a deep neural network (DNN) approach using the ship motions as input variables. The main advantage of such a method lies in its possibility to predict the spectral parameters without the use of ship specific properties.