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 theta(m), and directional spreading width sigma(theta). 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, theta(m), and sigma(theta), 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.