Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Strait and Its Adjacent Waters

Significant wave height (SWH) forecasting is a key process for offshore and costal engineering. However, accurate prediction of the SWH is quite challenging due to the randomness and fluctuation features of waves. This paper employs a novel deep learning method, the gated recurrent unit network (GRU), to forecast SWH with lead times of 3, 6, 12 and 24 h. The data sets used in this study include the wind speed of the past 3 h and the current SWH as inputs, which were obtained from six buoy stations in the Taiwan Strait and its adjacent waters. The GRU results are compared with those of back propagation neural network (BP), extreme learning machine (ELM), support vector machine (SVM), and random forest (RF). Although the error indices of the six stations are different, the general performance of GRU is satisfactory, with a faster forecasting speed, smaller volatility and better adaptability. Using buoy station 46714D as an example, the root mean square error (RMSE) predicted by GRU reaches 0.234, 0.299, 0.371, and 0.479 with lead times of 3, 6, 12, and 24 h, respectively.