Attention-enhanced deep learning approach for marine heatwave forecasting
文献类型：期刊论文
| 作者 | Liu, Yiyun1,2; Gao, Le1; Yang, Shuguo2 |
| 刊名 | ACTA OCEANOLOGICA SINICA
 |
| 出版日期 | 2025-04-15 |
| 页码 | 14 |
| 关键词 | sea surface temperature forecasting marine heatwave event detection deep learning attention mechanism |
| ISSN号 | 0253-505X |
| DOI | 10.1007/s13131-024-2424-6 |
| 通讯作者 | Gao, Le(gaole@qdio.ac.cn) |
| 英文摘要 | Marine heatwave (MHW) events refer to periods of significantly elevated sea surface temperatures (SST), persisting from days to months, with significant impacts on marine ecosystems, including increased mortality among marine life and coral bleaching. Forecasting MHW events are crucial to mitigate their harmful effects. This study presents a two-step forecasting process: short-term SST prediction followed by MHW event detection based on the forecasted SST. Firstly, we developed the "SST-MHW-DL" model using the ConvLSTM architecture, which incorporates an attention mechanism to enhance both SST forecasting and MHW event detection. The model utilizes SST data from the preceding 60 d to forecast SST and detect MHW events for the subsequent 15 d. Verification results for SST forecasting demonstrate a root mean square error (RMSE) of 0.64 degrees C, a mean absolute percentage error (MAPE) of 2.05%, and a coefficient of determination (R2) of 0.85, indicating the model's ability to accurately predict future temperatures by leveraging historical sea temperature information. For MHW event detection using forecasted SST, the evaluation metrics of "accuracy", "precision", and "recall" achieved values of 0.77, 0.73, and 0.43, respectively, demonstrating the model's capability to capture the occurrence of MHW events accurately. Furthermore, the attention-enhanced mechanism reveals that recent SST variations within the past 10 days have the most significant impact on forecasting accuracy, while variations in deep-sea regions and along the Taiwan Strait significantly contribute to the model's efficacy in capturing spatial characteristics. Additionally, the proposed model and temporal mechanism were applied to detect MHWs in the Atlantic Ocean. By inputting 30 d of SST data, the model predicted SST with an RMSE of 1.02 degrees C and an R2 of 0.94. The accuracy, precision, and recall for MHW detection were 0.79, 0.78, and 0.62, respectively, further demonstrating the model's robustness and usability. |
| WOS关键词 | SEA-SURFACE TEMPERATURE |
| 资助项目 | National Natural Science Foundation of China[42376175] ; National Natural Science Foundation of China[42090044] ; National Natural Science Foundation of China[U2006211] |
| WOS研究方向 | Oceanography |
| 语种 | 英语 |
| WOS记录号 | WOS:001466224700001 |
| 出版者 | SPRINGER |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/201593]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Gao, Le |
| 作者单位 | 1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Yiyun,Gao, Le,Yang, Shuguo. Attention-enhanced deep learning approach for marine heatwave forecasting[J]. ACTA OCEANOLOGICA SINICA,2025:14. |
| APA | Liu, Yiyun,Gao, Le,&Yang, Shuguo.(2025).Attention-enhanced deep learning approach for marine heatwave forecasting.ACTA OCEANOLOGICA SINICA,14. |
| MLA | Liu, Yiyun,et al."Attention-enhanced deep learning approach for marine heatwave forecasting".ACTA OCEANOLOGICA SINICA (2025):14. |
入库方式： OAI收割
来源：海洋研究所
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