Junsheng Zhang

This study proposes a Compressive Sensing Convolutional Long Short-Term Memory (CSCL) model that aims to improve short-term (12–24 h) forecast accuracy compared to standard ConvLSTM. It is especially useful when subtle spatiotemporal variations complicate feature extraction. CSCL uses uniform sampling to partially mask spatiotemporal wave fields. The model training strategy integrates both complete and masked samples from pre- and post-sampling. This design encourages the network to learn and amplify subtle distributional differences. Consequently, small variations in convolutional responses become more informative for feature extraction. We considered the theoretical explanations for why this sampling-augmented training enhances sensitivity to minor signals and validated the approach experimentally. For the region 120–140° E and 20–40° N, a four-layer CSCL model using the first five moments as inputs achieved the best prediction performance. Compared to ConvLSTM, the R² for significant wave height improved by 2.2–43.8% and for mean wave period by 3.7–22.3%. A wave-energy case study confirmed the model’s practicality. CSCL may be extended to the prediction of extreme events (e.g., typhoons, tsunamis) and other oceanic variables such as wind, sea-surface pressure, and temperature.

In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to accurately predict due to complex ocean conditions and the ubiquitous chaotic phenomena in nature. Therefore, this paper proposes an integrated CEEMDAN-LSTM joint model. Traditional computational fluid dynamics (CFD) has a long calculation period and high capital consumption, but artificial intelligence methods have the advantage of high accuracy and fast convergence. CEEMDAN is a commonly used method for digital signal processing in mechanical engineering, but has not yet been used for SWH prediction. It has better performance than the EMD and EEMD and is more suitable for LSTM prediction. In addition, this paper also proposes a novel filter formulation for SWH outliers based on the improved violin-box plot. The final empirical results show that CEEMDAN-LSTM significantly outperforms LSTM for each forecast duration, significantly improving the prediction accuracy. In particular, for a forecast duration of 1 h, CEEMDAN-LSTM has the most significant improvement over LSTM, with 71.91% of RMSE, 68.46% of MAE and 6.80% of NSE, respectively. In summary, our model can improve the real-time scheduling capability for marine engineering maintenance and operations.