Spatial-temporal Offshore Current Field Forecasting Using Residual-learning Based Purely CNN Methodology with Attention Mechanism
Spatial-temporal current forecasting is indispensable for ocean engineering and marine science exploration, for instance aiding in the conservation and protection of marine ecosystems, planning shipping-routes and determining the length and fuel consumption of sea-going voyages, obtaining deeper ins...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2323827 |
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| Summary: | Spatial-temporal current forecasting is indispensable for ocean engineering and marine science exploration, for instance aiding in the conservation and protection of marine ecosystems, planning shipping-routes and determining the length and fuel consumption of sea-going voyages, obtaining deeper insights into the distribution of heat flux within the ocean, which is vital for better understanding climate changes, and so on. Most present related-studies primarily focused on single location or grid-cell-based forecasting, such methodologies are site-specific and neglect the importance of spatial-temporal fidelity. Furtherly, the Recurrent Neural Networks-based methods previously employed exhibit low efficiency in terms of model convergence concerning practical engineering purposes, and numerical weather models are time-consuming and computational expensive. A newly improved Unet-based model using residual-learning with attention strategy is proposed for 2D sea surface current (SSC) velocity predictions with a more efficient perspective. Several machine-learning methodologies were adopted for a better performance comparison. The final predictions demonstrated its superiorities that the proposed neural-learning method outperforms the other established approaches with spatial-resolved mean RMSE less than 0.009 m/s and 0.006 m/s. As a promising surrogate for SSC predictions, the proposed methodology has strong potential in operation marine monitoring and engineering constructions. |
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| ISSN: | 0883-9514 1087-6545 |