Prediction of Significant Wave Height Based on Gated Recurrent Unit and Sequence-to-Sequence Networks in the Taiwan Strait

Abstract

Wave forecasting approaches based on deep learning techniques have recently made a great progress. In this study, we developed a deep learning model based on Gated Recurrent Unit (GRU) and sequence-to-sequence neural networks (GRUS), to improve the forecasting accuracy of significant wave heights for the Taiwan Strait, where ocean waves and winds own their unique characteristics. The performances of our proposed GRUS model and the other deep learning models based on WaveNet and Long Short-Term Memory (LSTM) were compared by means of wind and wave observations at three buoys in the study area. Model parameters were optimized by means of various model experiments. Performance comparison illustrates that our proposed GRUS model outperforms the other models in 24-hour H_s forecasting, while the GRUS has extraordinary ability for short-term prediction (prediction horizon is less than 6 h). Moreover, for high wave states prediction (e.g., wave height over 4 m), the GRUS has the strongest prediction ability among the models, in which forecasted wave heights are mostly lower than the corresponding observations.