Articles | Volume 19, issue 6
https://doi.org/10.5194/os-19-1561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-1561-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Lei Han
CORRESPONDING AUTHOR
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Lifang Jiang
South China Sea Forecast and Disaster Reduction Center, Ministry of Natural Resources, Guangzhou 510000, China
Yuting Zhang
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Minghong Xie
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Yu Liu
Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
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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.
Accurate wave forecasts are essential to marine engineering safety. The research designs a model...