A training strategy to improve the generalization capability of deep learning-based significant wave height prediction models in offshore China.

Accurate forecasting of significant wave height (SWH) is crucial for ensuring the safety of marine navigation. To achieve good accuracy, deep learning models need to be trained separately using SWH data from all forecast locations. However, improving the generalizability of SWH prediction models is vital for practical engineering purposes. This paper proposes a training strategy that utilizes multi-point data fusion by using wave data obtained from different locations to construct a training dataset. To verify the feasibility of training strategy, the artificial neural network (ANN), long short-term memory (LSTM), and temporal convolutional network (TCN) models are used to test in China offshore. The RMSE and MAPE of different models with 2h advance forecasts are almost 0.05 and 4%, indicating that the models have good forecasting performance. Additionally, the models exhibit consistency with the model trained solely by single position data, which demonstrates that the training strategy applies to various models. In addition, in sea areas without wave training data, two locations are randomly selected and used in the pre-trained model to predict SWH. The various models demonstrate favorable forecasting results, further demonstrating that the training strategy proposed in this study can enhance the generalizability of models. • The training method based on multi-point data fusion can improve the spatial generalization of the SWH forecasting model. • Using ANN, LSTM, and TCN models for significant wave height forecasting at different locations. • The prediction accuracy of the multi-point data fusion training method and single location training method is the same. • The training method based on multi-point data fusion has good applicability to different deep learning models. [ABSTRACT FROM AUTHOR]