Instantaneous prediction of irregular ocean surface wave based on deep learning

Xu, Gang and Zhang, Siwen and Shi, Weichao (2023) Instantaneous prediction of irregular ocean surface wave based on deep learning. Ocean Engineering, 267. 113218. ISSN 0029-8018 (https://doi.org/10.1016/j.oceaneng.2022.113218)

[thumbnail of Xu-etal-OE-2022-Instantaneous-prediction-of-irregular-ocean-surface-wave-based-on-deep-learning]
Preview
 Text. Filename: Xu_etal_OE_2022_Instantaneous_prediction_of_irregular_ocean_surface_wave_based_on_deep_learning.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (11MB)| Preview

Abstract

Ocean surface wave environment due to its randomness has mostly been analyzed and understood from a statistical way. Accurate and reliable wave prediction is the basic guarantee for ocean transportation and offshore operation. However, the significant nonlinearity of the ocean surface wave in real sea will result in a simulation and prediction challenge. To solve the problem, this paper proposes a Deep Learning instantaneous prediction model based on a Convolutional Neural Network-Long Short Term Memory model (CNN-LSTM) and wavelet function proven as the activation function of the neural network model to strengthen the fitting of the nonlinear mapping relationship between waves appearing in two successive different time periods. And the results shows that the wavelet activation function has better generalization ability than the traditional activation function for irregular wave data. In the numerical verification test, the two-parameter spectrum of International Towing Tank Conference (ITTC) and the combined model of extreme wave and random wave are used to simulate the irregular wave. The standardized time series of free surface elevation is made into a data set for model training and verification. In order to improve the performance of the model as accurate as possible, main parameters of the model are investigated in this paper. The optimal parameters are obtained and the model tuning suggestion is developed for future studies.

ORCID iDs

Xu, Gang, Zhang, Siwen and Shi, Weichao ORCID logoORCID: https://orcid.org/0000-0001-9730-7313;
CORE (COnnecting REpositories)