A hybrid significant wave height prediction model based on ensemble empirical mode decomposition and generative adversarial networks

Jinyuan Mo; Qi Wang; Kang Yang; Qideng Tang; Kaiwen Li; Rui Wang

doi:10.1117/12.3004666

9 October 2023 A hybrid significant wave height prediction model based on ensemble empirical mode decomposition and generative adversarial networks

Jinyuan Mo, Qi Wang, Kang Yang, Qideng Tang, Kaiwen Li, Rui Wang

Author Affiliations +

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911S (2023) https://doi.org/10.1117/12.3004666
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Short-term prediction of ocean significant wave height is crucial for human maritime activities. However, historical wave height data possess characteristics such as high complexity and strong randomness. To address these issues, this paper proposes a hybrid model that combines Ensemble Empirical Mode Decomposition (EEMD) and Generative Adversarial Networks (GANs). The model first decomposes the raw wave height data using EEMD to select effective sub-series for reconstruction and implements denoising of the original data. Then, the reconstructed wave height sequence and relevant features are fed into a GAN-based prediction model. The generator in the model comprises of Long Short-Term Memory Network (LSTM) networks, while the discriminator consists of Convolutional Neural Networks (CNNs). Finally, the generator and discriminator are trained adversarially using North Pacific oceanographic data to achieve short-term predictions of ocean wave height. This proposed model is then compared with three baseline models, and the results show that our model performs the best in all three evaluation metrics, making it a valuable tool for ocean engineering.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinyuan Mo, Qi Wang, Kang Yang, Qideng Tang, Kaiwen Li, and Rui Wang "A hybrid significant wave height prediction model based on ensemble empirical mode decomposition and generative adversarial networks", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911S (9 October 2023); https://doi.org/10.1117/12.3004666

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KEYWORDS

Data modeling

Modal decomposition

Denoising

Signal processing

Performance modeling

Binary data

Autoregressive models

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