Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1561-2023
https://doi.org/10.5194/os-19-1561-2023
Research article
 | 
09 Nov 2023
Research article |  | 09 Nov 2023

Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm

Qiyan Ji, Lei Han, Lifang Jiang, Yuting Zhang, Minghong Xie, and Yu Liu

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Cited articles

Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks, arXiv, abs/1803.01271, https://doi.org/10.48550/arXiv.1803.01271, 2018. 
Bento, P. M. R., Pombo, J. A. N., Mendes, R. P. G., Calado, M. R. A., and Mariano, S. J. P. S.: Ocean wave energy forecasting using optimised deep learning neural networks, Ocean Eng., 219, 108372, https://doi.org/10.1016/j.oceaneng.2020.108372, 2021. 
Bisoi, R., Dash, P. K., and Parida, A. K.: Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis, Appl. Soft Comput., 74, 652–678, https://doi.org/10.1016/j.asoc.2018.11.008, 2019. 
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1. Model description and validation, J. Geophys. Res.-Ocean., 104, 7649–7666, https://doi.org/10.1029/98jc02622, 1999. 
Bretschneider, C. L.: Hurricane design – Wave practices, J. Waterways Harb. Div., 124, 39–62, 1957. 
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Short summary
Accurate wave forecasts are essential to marine engineering safety. The research designs a model with combined signal decomposition and multiple neural network algorithms to predict wave parameters. The hybrid wave prediction model has good robustness and generalization ability. The contribution of the various algorithms to the model prediction skill was analyzed by the ablation experiments. This work provides a neoteric view of marine element forecasting based on artificial intelligence.