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.
Research article
| 
09 Nov 2023
Research article |
| 09 Nov 2023
| 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
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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Cited
21 citations as recorded by crossref.
- Short-term significant wave height prediction based on adaptive two-layer decomposition and BiLSTM-attention model J. Jiao et al. https://doi.org/10.1016/j.oceaneng.2026.126187
- Multi-Scale temporal modeling via STL decomposition and dual-Transformer for resource prediction X. Yu et al. https://doi.org/10.1016/j.future.2025.108323
- Multi-step significant wave height prediction model based on feature enhancement compression, mode decomposition, multi-path convolutional recurrent network and regression correction G. Li et al. https://doi.org/10.1016/j.measurement.2025.118401
- Wave Height Forecasting in the Bay of Bengal Using Multivariate Hybrid Deep Learning Models P. Thet et al. https://doi.org/10.3390/jmse13081412
- Advance in Significant Wave Height Prediction: A Comprehensive Survey J. Mo et al. https://doi.org/10.23919/CSMS.2024.0019
- Significant wave height prediction based on improved fuzzy C-means clustering and bivariate kernel density estimation J. Zhou et al. https://doi.org/10.1016/j.renene.2025.122787
- VMD-LSTM-Based Model Predictive Control for Hybrid Energy Storage Systems with Auto-Tuning Weights and Constraints Y. Yang et al. https://doi.org/10.3390/en18215559
- A hybrid network with DNN and WGAN for supercontinum prediction D. Yang et al. https://doi.org/10.1016/j.yofte.2024.103816
- Significant Wave Height Forecasting Method for the North Atlantic Ocean Based on the CEEMDAN-iTransformer Model M. Chu et al. https://doi.org/10.3390/jmse14110994
- Dynamics in salinity diffusion influenced by anthropogenic pressures and climate change: a case study of the Aghien lagoon (Abidjan, Côte d’Ivoire) B. Goe et al. https://doi.org/10.1080/15715124.2025.2477791
- Multi-Scale Forecasting of Natural Rubber Prices Using VMD-Augmented BiLSTM: A Hybrid Architecture Ablation Study M. Pinitjitsamut https://doi.org/10.3390/forecast8030043
- Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments C. Li et al. https://doi.org/10.3390/w16020302
- Hybrid singular spectrum analysis and LSTM modeling for daily precipitation forecasting Z. Lu et al. https://doi.org/10.1016/j.geog.2025.12.007
- A Significant Wave Height Prediction Method Based on Improved Temporal Convolutional Network and Attention Mechanism Y. Han et al. https://doi.org/10.3390/electronics13244879
- Coastal short term wave power prediction based on deep learning model Y. Wu et al. https://doi.org/10.1016/j.renene.2025.124700
- A Trend and Seasonal Fusion Network Considering Extremum-Sensitivity for Urban Distributed Photovoltaic Power Forecasting J. Wang et al. https://doi.org/10.1016/j.renene.2026.126092
- A hybrid model based on a dual-layer decomposition framework and LSTM-Informer for significant wave height prediction S. Xiong et al. https://doi.org/10.1016/j.oceaneng.2026.124412
- Physics-informed temporal convolutional network with auto-regressive residual for significant wave height prediction J. Li et al. https://doi.org/10.1016/j.oceaneng.2025.121150
- Constant frequency mapping mechanism for WECs and FPM-Net predictive control J. Wang et al. https://doi.org/10.1016/j.oceaneng.2026.125702
- A stochastic multivariate extreme-value model for storm and wave climate off the west coast of India R. Mahesh et al. https://doi.org/10.1016/j.oceaneng.2026.124897
- Analysis and prediction of solid oxide electrolysis stack's aging trajectory considering nonstationary phenomenon based on VMD-TCN-LSTM M. Zhang et al. https://doi.org/10.1016/j.ijhydene.2025.151304
21 citations as recorded by crossref.
- Short-term significant wave height prediction based on adaptive two-layer decomposition and BiLSTM-attention model J. Jiao et al. https://doi.org/10.1016/j.oceaneng.2026.126187
- Multi-Scale temporal modeling via STL decomposition and dual-Transformer for resource prediction X. Yu et al. https://doi.org/10.1016/j.future.2025.108323
- Multi-step significant wave height prediction model based on feature enhancement compression, mode decomposition, multi-path convolutional recurrent network and regression correction G. Li et al. https://doi.org/10.1016/j.measurement.2025.118401
- Wave Height Forecasting in the Bay of Bengal Using Multivariate Hybrid Deep Learning Models P. Thet et al. https://doi.org/10.3390/jmse13081412
- Advance in Significant Wave Height Prediction: A Comprehensive Survey J. Mo et al. https://doi.org/10.23919/CSMS.2024.0019
- Significant wave height prediction based on improved fuzzy C-means clustering and bivariate kernel density estimation J. Zhou et al. https://doi.org/10.1016/j.renene.2025.122787
- VMD-LSTM-Based Model Predictive Control for Hybrid Energy Storage Systems with Auto-Tuning Weights and Constraints Y. Yang et al. https://doi.org/10.3390/en18215559
- A hybrid network with DNN and WGAN for supercontinum prediction D. Yang et al. https://doi.org/10.1016/j.yofte.2024.103816
- Significant Wave Height Forecasting Method for the North Atlantic Ocean Based on the CEEMDAN-iTransformer Model M. Chu et al. https://doi.org/10.3390/jmse14110994
- Dynamics in salinity diffusion influenced by anthropogenic pressures and climate change: a case study of the Aghien lagoon (Abidjan, Côte d’Ivoire) B. Goe et al. https://doi.org/10.1080/15715124.2025.2477791
- Multi-Scale Forecasting of Natural Rubber Prices Using VMD-Augmented BiLSTM: A Hybrid Architecture Ablation Study M. Pinitjitsamut https://doi.org/10.3390/forecast8030043
- Comparisons of Different Machine Learning-Based Rainfall–Runoff Simulations under Changing Environments C. Li et al. https://doi.org/10.3390/w16020302
- Hybrid singular spectrum analysis and LSTM modeling for daily precipitation forecasting Z. Lu et al. https://doi.org/10.1016/j.geog.2025.12.007
- A Significant Wave Height Prediction Method Based on Improved Temporal Convolutional Network and Attention Mechanism Y. Han et al. https://doi.org/10.3390/electronics13244879
- Coastal short term wave power prediction based on deep learning model Y. Wu et al. https://doi.org/10.1016/j.renene.2025.124700
- A Trend and Seasonal Fusion Network Considering Extremum-Sensitivity for Urban Distributed Photovoltaic Power Forecasting J. Wang et al. https://doi.org/10.1016/j.renene.2026.126092
- A hybrid model based on a dual-layer decomposition framework and LSTM-Informer for significant wave height prediction S. Xiong et al. https://doi.org/10.1016/j.oceaneng.2026.124412
- Physics-informed temporal convolutional network with auto-regressive residual for significant wave height prediction J. Li et al. https://doi.org/10.1016/j.oceaneng.2025.121150
- Constant frequency mapping mechanism for WECs and FPM-Net predictive control J. Wang et al. https://doi.org/10.1016/j.oceaneng.2026.125702
- A stochastic multivariate extreme-value model for storm and wave climate off the west coast of India R. Mahesh et al. https://doi.org/10.1016/j.oceaneng.2026.124897
- Analysis and prediction of solid oxide electrolysis stack's aging trajectory considering nonstationary phenomenon based on VMD-TCN-LSTM M. Zhang et al. https://doi.org/10.1016/j.ijhydene.2025.151304
Saved (final revised paper)
Latest update: 08 Jul 2026
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...
