A Wave Prediction Framework Based on Machine Learning and the Third Generation Wave Model

To predict the evolution of wave spectrum in the real ocean, a machine learning framework is developed by training a long short-term memory (LSTM) neural network on a physics-based third-generation wave model (Simulating WAve Nearshore (SWAN)). Considering the realistic ocean waves are usually mixtures of windsea and swells, the wave spectrum is partitioned using a watershed algorithm, such that the windsea and swells are analyzed and predicted separately. Four parameters are selected to capture the wave spectrum of each system, including the significant wave height H_s, peaked wave period T_p, mean propagation direction θ_m, and directional spreading width $σθ$⁠. The results demonstrate that the LSTM neural network can achieve accurate prediction of wave condition, the mean absolute error percentage (MAEPs) of 1-h prediction is less than 5.9%, 3.3%, 3.5%, and 3.3% for H_s, T_p, θ_m, and $σθ$⁠, respectively, and accurate prediction of wave spectra is achieved. Even for the 10-h prediction, satisfactory results are obtained, e.g., the MAEP of H_s is less than 15.5%. The effects of output size (i.e., prediction duration), input data size (i.e., number of delays), as well as different combinations of input features on predictions of wave conditions are examined.