Surface current prediction in the seas around the Korean peninsula using a CNN-based deep-learning model

Prediction of sea surface current is essential for various marine activities, such as tourist industry, commercial transportation, fishing industries, search and rescue operations, and so on. Numerical forecast models make it possible to predict a realistic ocean with the help of data-assimilation and fine spatial resolution. Nevertheless, complicated numerical prediction model requires heavy power and time for computation, which initiated development of novel approaches with efficient computational costs. In that sense, artificial neural networks could be one of the solutions because they need low computational power for prediction thanks to pre-trained networks. Here, we present a prediction framework applicable to the surface current prediction in the seas around the Korean peninsula using three-dimensional (3-D) convolutional neural networks. The network is based on the 3-D U-net structure and modified to predict sea surface currents using oceanic and atmospheric variables. In the forecast procedure, it is optimized to minimize the error of the next day’s sea surface current field and its recursively predicting structure allows more days to be predicted. The network’s performance is evaluated by changing input days and variables to find the optimal surface-current-prediction artificial neural network model, which demonstrates its strong potential for practical uses near future.