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 were both considered in the proposed model. The wave data following a JONSWAP (Joint North Sea Wave Project) spectrum measured through an indoor wave tank experiment were used for the study. The measured time series were transformed to the frequency domain by the fast Fourier transform and divided into five bands by a filtering method. With the time series corresponding to each band, the LSSVM model was 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 for the application of short-term real-time wave prediction. Both wave elevations and wave forces were studied as applications of the model.
Real ocean waves can be characterized as irregular, random, and highly nonlinear. Although theoretical and numerical models have long been developed to understand the behavior of ocean waves, it remains a challenge to make real-time predictions. A variety of prediction methods have been developed. For computational models, wave parameters are simulated and predicted by solving equations based on theoretical models, such as the wave model proposed by the Wamdi Group (1988). This method could make good predictions for waves in deep-sea areas, but it has certain limitations of application for nearshore areas with complex terrain. In recent years, artificial intelligence methods such as machine learning techniques have attracted much attention from researchers in various fields because the emergence of these methods effectively makes up for the shortcomings of conventional prediction methods. The machine learning (ML) method could make statistical predictions based on historical data, which has been widely applied in the prediction of energy consumption, traffic flow, rainfall, stock price and other fields, and has great potential in marine engineering applications (Gopinath and Dwarakish, 2015).