OSP-FEAN: Optimizing Significant Wave Height Prediction with Feature Engineering and Attention Network.

Accurately forecasting significant wave height (SWH) is meaningful since SWH is an essential parameter in coastal and ocean engineering. In order to accurately predict SWH, we propose the OSP-FEAN method, which optimizes significant wave height prediction by feature engineering and attention network. Specifically, we conduct feature engineering by adding the first-order to twelfth-order lag variables of SWH to the input set for feature enhancement and using the random forest algorithm for feature selection. Moreover, we construct a sequence to sequence neural network. In order to improve the forecast accuracy, we add an attention mechanism based on the memory layer to this neural network. Finally, extensive experiments with observed data at different stations are conducted to verify the effectiveness of our method on 6-h, 12-h and 24-h predictions, especially the superiority in outlier prediction.