Significant wave height (SWH) prediction plays an important role in marine engineering areas such as fishery, exploration, power generation, and ocean transportation. For long-term forecasting of a specific location, classical numerical model wave height forecasting methods often require detailed climatic data and incur considerable calculation costs, which are often impractical in emergencies. In addition, how to capture and use the dynamic correlation between multiple variables is also a major research challenge for multivariate SWH prediction. To explore a new method for predicting SWH, this paper proposes a deep neural network model for multivariate time series SWH prediction—namely, CLTS-Net. In this study, the sea surface wind and wave height in the ERA5 dataset of the relevant points P1, P2, and P3 from 2011 to 2018 were used as input information to train the model and evaluate the model’s SWH prediction performance. The results show that the correlation coefficients (R) of CLTS-Net are 0.99 and 0.99, respectively, in the 24 h and 48 h SWH forecasts at point P1 along the coast. Compared with the current mainstream artificial intelligence-based SWH solutions, it is much higher than ANN (0.79, 0.70), RNN (0.82, 0.83), LSTM (0.93, 0.91), and Bi-LSTM (0.95, 0.94). Point P3 is located in the deep sea. In the 24 h and 48 h SWH forecasts, the R of CLTS-Net is 0.97 and 0.98, respectively, which are much higher than ANN (0.71, 0.72), RNN (0.85, 0.78), LSTM (0.85, 0.78), and Bi-LSTM (0.93, 0.93). Especially in the 72 h SWH forecast, when other methods have too large errors and have lost their practical application value, the R of CLTS-Net at P1, P2, and P3 can still reach 0.81, 0.71, and 0.98. The results also show that CLTS-Net can capture the short-term and long-term dependencies of data, so as to accurately predict long-term SWH, and has wide applicability in different sea areas.
Significant wave height (SWH) prediction plays an important role in marine engineering fields such as fishery, exploration, power generation, and ocean transportation. Traditional SWH prediction methods based on numerical models cannot achieve high accuracy. In addition, the current SWH prediction methods are largely limited to single-point SWH prediction, without considering regional SWH prediction. In order to explore a new SWH prediction method, this paper proposes a deep neural network model for regional SWH prediction based on the attention mechanism, namely CBA-Net. In this study, the wind and wave height of the ERA5 data set in the South China Sea from 2011 to 2018 were used as input features to train the model to evaluate the SWH prediction performance at 1 h, 12 h, and 24 h. The results show that the single use of a convolutional neural network cannot accurately predict SWH. After adding the Bi-LSTM layer and attention mechanism, the prediction of SWH is greatly improved. In the 1 h SWH prediction using CBA-Net, SARMSE, SAMAPE, SACC are 0.299, 0.136, 0.971 respectively. Compared with the CNN + Bi-LSTM method that does not use the attention mechanism, SARMSE and SAMAPE are reduced by 43.4% and 48.7%, respectively, while SACC is increased by 5%. In the 12 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.379, 0.177, 0.954 respectively. In the 24 h SWH prediction, SARMSE, SAMAPE, and SACC of CBA-Net are 0.500, 0.236, 0.912 respectively. Although with the increase of prediction time, the performance is slightly lower than that of 12 h, the prediction error is still maintained at a small level, which is still better than other methods.
Significant wave height (SWH) prediction can effectively improve the safety of marine activities and reduce the occurrence of maritime accidents, which is of great significance to national security and the development of the marine economy. In this study, we comprehensively analyzed the SWH prediction performance of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) by considering different input lengths, prediction lengths, and model complexity. The experimental results show that (1) the input length impacts the prediction results of SWH, but it does not mean that the longer the input length, the better the prediction performance. When the input length is 24h, the prediction performance of RNN, LSTM, and GRU models is better. (2) The prediction length influences the SWH prediction results. As the prediction length increases, the prediction performance gradually decreases. Among them, RNN is not suitable for 48h long-term SWH prediction. (3) The more layers of the model, the better the SWH prediction performance is not necessarily. When the number of layers is set to 3 or 4, the model’s prediction performance is better.
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