Multiprior Knowledge-Guided Deep Learning Model for Kuroshio Loop Current Intrusion Prediction in South China Sea
文献类型：期刊论文
| 作者 | Zhou, Yuan1; Yang, Mingzhe1; Chen, Keran1; Li, Xiaofeng2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:14 |
| 关键词 | Predictive models Deep learning Remote sensing Forecasting Accuracy Spatiotemporal phenomena Satellites Indexes Tensors Spatial resolution field prediction Kuroshio intrusion (KI) satellite remote sensing data spatiotemporal prediction |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3627103 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | The Kuroshio intrusion (KI) into South China Sea (SCS) via Luzon Strait significantly influences regional ocean dynamics. However, predicting this intrusion, especially the Kuroshio Loop Current (KLC), remains challenging due to its complex mesoscale and submesoscale processes. Traditional physical models struggle to capture the nonlinear and multiscale features of the KI, while deep learning approaches face challenges in incorporating the essential physical processes that characterize the KLC. To address these challenges, we developed the KI Forecast Network (KIFnet), a multiprior knowledge-guided deep learning model. KIFnet integrates physical oceanographic principles with data-driven predictions, enhancing its ability to capture complex ocean dynamics. KIFnet incorporates an SST-guided SSH prediction module and a vorticity-guided loss function to explicitly model thermal and dynamic features of the KLC, advancing the challenging task of forecasting KLC intrusion events. Experimental results demonstrate that the model achieves an accuracy of 88% for KLC intrusion events and provides reliable predictions up to ten days ahead. Prior limitations in KLC forecasting have constrained SCS climate modeling and marine ecosystem management. KIFnet provides accurate KLC predictions, supporting proactive climate adaptation and sustainable ecosystem strategies. |
| WOS关键词 | NETWORK |
| 资助项目 | National Natural Science Foundation of China[62171320] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001626459000021 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/204333]  |
| 专题 | 中国科学院海洋研究所 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Shandong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Yuan,Yang, Mingzhe,Chen, Keran,et al. Multiprior Knowledge-Guided Deep Learning Model for Kuroshio Loop Current Intrusion Prediction in South China Sea[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:14. |
| APA | Zhou, Yuan,Yang, Mingzhe,Chen, Keran,&Li, Xiaofeng.(2025).Multiprior Knowledge-Guided Deep Learning Model for Kuroshio Loop Current Intrusion Prediction in South China Sea.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,14. |
| MLA | Zhou, Yuan,et al."Multiprior Knowledge-Guided Deep Learning Model for Kuroshio Loop Current Intrusion Prediction in South China Sea".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):14. |
入库方式： OAI收割
来源：海洋研究所
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