Articles | Volume 18, issue 2
https://doi.org/10.5194/os-18-419-2022
© Author(s) 2022. 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-18-419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Mar 2022
Research article |
| 28 Mar 2022
| 28 Mar 2022
Forecasting hurricane-forced significant wave heights using a long short-term memory network in the Caribbean Sea
Brandon J. Bethel
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Wenjin Sun
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Changming Dong
CORRESPONDING AUTHOR
School of Marine Sciences, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Southern Ocean Science and Engineering Guangdong Laboratory
(Zhuhai), Zhuhai 519000, China
Department of Atmospheric and Oceanic Sciences, University of
California, Los Angeles, CA 90095, USA
Dongxia Wang
China State Shipbuilding Corporation (Chongqing) Haizhuang
Windpower Equipment Co., Ltd., Chongqing 400021, China
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35 citations as recorded by crossref.
- A hybrid model for significant wave height prediction based on an improved empirical wavelet transform decomposition and long-short term memory network J. Wang et al. https://doi.org/10.1016/j.ocemod.2024.102367
- Deep Learning-Based Enhanced ISAR-RID Imaging Method X. Wang et al. https://doi.org/10.3390/rs15215166
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- Prediction of significant wave height based on EEMD and deep learning T. Song et al. https://doi.org/10.3389/fmars.2023.1089357
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- ASTMEN: an adaptive spatiotemporal and multi-element fusion network for ocean surface currents forecasting X. Li et al. https://doi.org/10.3389/fmars.2023.1281387
- Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy J. Wang et al. https://doi.org/10.1063/5.0214806
- Noise-augmented and probabilistic deep learning for significant wave height forecasting with attention-based LSTM models Z. Ren et al. https://doi.org/10.1016/j.apor.2026.105016
- An effective deep learning model for spatial-temporal significant wave height prediction in the Atlantic hurricane area Z. Ouyang et al. https://doi.org/10.1016/j.oceaneng.2024.120083
- Significant wave height prediction based on deep learning in the South China Sea P. Hao et al. https://doi.org/10.3389/fmars.2022.1113788
- Application of deep learning in estimating the convective mixing induced by brine rejection X. Gao et al. https://doi.org/10.1016/j.ocemod.2024.102314
- Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature J. Mutinda et al. https://doi.org/10.1155/ijmm/4795841
- Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates T. Du et al. https://doi.org/10.1016/j.oceaneng.2026.124560
- Suspended sediment concentration prediction using a hybrid VMD-LSTM-SA-ensemble model T. Ding et al. https://doi.org/10.1016/j.ijsrc.2025.12.003
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- Multi-Horizon Significant Wave Height Forecasting Along Guangdong–Hong Kong Coast Using Tropical Cyclone Trajectory Information R. Zhou et al. https://doi.org/10.3390/jmse14181750
- Applications of deep learning in physical oceanography: a comprehensive review Q. Zhao et al. https://doi.org/10.3389/fmars.2024.1396322
- Stock price prediction using combined GARCH-AI models J. Mutinda & A. Langat https://doi.org/10.1016/j.sciaf.2024.e02374
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- Improving multi-variable wave forecasting with AI: Integrating LSTM and random forest, using a window and flatten technique N. Portillo Juan et al. https://doi.org/10.1016/j.ocemod.2025.102638
- Recent Developments in Artificial Intelligence in Oceanography C. Dong et al. https://doi.org/10.34133/2022/9870950
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- A comparison of LSTM-based Ensemble models for wind speed prediction in the Atlantic Ocean using optimised parameters N. Cardenas-Escobar et al. https://doi.org/10.1007/s12145-025-01815-y
- Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions M. Varentsov et al. https://doi.org/10.3390/cli11100200
- HIGH-ACCURACY WAVE FORECASTING FOR THE SEAS AROUND JAPAN USING A BIDIRECTIONAL CONVLSTM Y. AOKI & R. NAKAMURA https://doi.org/10.2208/jscejj.26-18017
- Prediction of significant wave height using a VMD-LSTM-rolling model in the South Sea of China T. Ding et al. https://doi.org/10.3389/fmars.2024.1382248
- A PCHIP based temporal refinement in ML for wave forecasting and wave power estimation J. Vijay et al. https://doi.org/10.1016/j.rineng.2026.110996
- Experimental validation of a wave-height sensor based on a MEMS piezoresistive cantilever and a waterproof film K. Shimada et al. https://doi.org/10.35848/1347-4065/ad9fc4
- Forecasting sea surface temperature during typhoon events in the Bohai Sea using spatiotemporal neural networks H. He et al. https://doi.org/10.1016/j.atmosres.2024.107578
- Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany M. Walczewski & H. Wöhrle https://doi.org/10.3390/en17040844
- Real and long-time predicting the trajectories of internal solitary waves: Case studies in the Sulu Sea L. Huang et al. https://doi.org/10.1016/j.oceaneng.2025.120765
- SALSTM: segmented self-attention long short-term memory for long-term forecasting Z. Dai et al. https://doi.org/10.1007/s11227-024-06493-z
- Assessing long short-term memory network significant wave height forecast efficacy in the Caribbean Sea and Northwestern Atlantic Ocean B. Bethel et al. https://doi.org/10.1016/j.oceaneng.2024.120045
- A PINN-based wave model for prediction of wave period and wavelength using dispersion relation J. Wang et al. https://doi.org/10.1016/j.oceaneng.2025.122504
- A hybrid CEEMDAN-VMD-TimesNet model for significant wave height prediction in the South Sea of China T. Ding et al. https://doi.org/10.3389/fmars.2024.1375631
Saved (final revised paper)
Latest update: 08 Nov 2026
Short summary
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore structures and coastal communities. As such, forecasting their evolution is of utmost importance. This study investigates the usage of a long short-term memory neural network to forecast hurricane-forced waves in the Caribbean Sea. Results strongly suggest that forecasts can be performed to a high degree of accuracy up to 12 h at minimal computational expense and even outperform a wave model.
Ocean surface waves forced by tropical cyclones can cause tremendous damage to offshore...
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