Abstract
Ocean wave height is an essential parameter for ocean engineering construction, planning decisions, and coastal hazards assessment. Long-term, accurate, and reliable ocean wave height forecasts are critical for the purposes mentioned above and have attracted more attention in recent years. This work proposed a novel method that achieves robust short-term and long-term ocean wave forecasting via the gate recurrent unit (GRU) network. The GRU-based wave forecasting model is established to learn long-term dependency among multivariate sequential data. The future wave height is predicted based on learned features via the proposed method. Case studies of 6 different stations along the coast of China are investigated. The results show that for 1-hour forecasts, the GRU network is superior to comparison methods in terms of all the error metrics. For 3-hour forecasts, the GRU network shows more robustness compared to the LSTM algorithm. The results also validate that the presented scheme is an efficient and reliable short-term and long-term wave forecasting approach. Applying the forecasting method in reality is essential for ocean safety, ocean exploitation, and many other fields.
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