STG-OceanWaveNet: Spatio-temporal geographic information guided ocean wave prediction network

Abstract

This study proposes a spatio-temporal geographical information-guided neural network to predict multi-step ahead space-time series of ocean waves. The network is designed to learn the ocean wave dynamics from external atmospheric forcing and internal wave processes. It also captures the nonlinear relationships in multiple input and at the spatial and temporal levels and shares their dependencies. The model learns these dependencies through a convoluted encoded latent feature, while a decoder predicts the space-time series of ocean waves from the latent representations. The model is trained on 35 years of a state-of-the-art global reanalysis dataset produced at 1-hour temporal and 25 km spatial resolutions around the Korean Peninsula. It is evaluated by predicting the same resolution’s multi-step ahead space-time series of ocean waves for a 48-hour forecast lead time for the 5 years not used for training. We conducted an ablation experiment to determine the optimal model architecture, input variable, and training period. The prediction results are compared and analyzed with the in-situ ocean wave measurements at the 18 observation stations. We consider the prediction results according to the start time of prediction with the in-situ measurements and hindcast results to examine the performance on the high waves that caused wave-induced disaster.