A machine learning model for wave prediction based on support vector machine

In this paper, we propose a least square support vector machine (LSSVM) model to predict ocean wave elevations in a random sea state. The frequency and time domain characteristics of historical wave data are both considered in the proposed model. The wave data following a JONSWAP spectrum measured through an indoor wave tank experiment are used for the study. The measured time series were transformed to frequency domain by the fast Fourier transform and divided into five bands by filtering method. With the time series corresponding to each band, the LSSVM model is trained separately and used to predict future time series. The proposed model is shown to greatly extend the prediction time length, making it more effective to the application of the short-term real-time wave prediction.