A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity
Accurate mid-short term prediction of sea surface salinity (SSS) is essential for operational ocean monitoring, particularly for capturing short-term salinity fluctuations that affect regional ocean dynamics and weather conditions. However, existing models struggle to extract complex spatiotemporal...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2548008 |
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| author | Guangwen Peng Yingbing Liu Cong Xiao Wenying Du Changjiang Xiao |
| author_facet | Guangwen Peng Yingbing Liu Cong Xiao Wenying Du Changjiang Xiao |
| author_sort | Guangwen Peng |
| collection | DOAJ |
| description | Accurate mid-short term prediction of sea surface salinity (SSS) is essential for operational ocean monitoring, particularly for capturing short-term salinity fluctuations that affect regional ocean dynamics and weather conditions. However, existing models struggle to extract complex spatiotemporal dependencies and are often limited to local regions, reducing their global applicability. To address these challenges, we propose a Dynamic Adaptive Graph Convolutional Recurrent Network (DAGCRN) for global SSS prediction. The DAGCRN employs an encoder–decoder architecture, where both the encoder and decoder integrate adaptive graph convolutional recurrent units (AGCRUs) and gated recurrent units (GRUs). AGCRUs dynamically construct topological relationships via graph convolution to model spatial variations, while GRUs capture temporal dependencies. This enables DAGCRN to effectively model the nonlinear and dynamic nature of global SSS variations. We evaluate DAGCRN's performance on the ESA Sea Surface Salinity CCI v3.21 dataset, which provides global gridded SSS observations from February 2010 to September 2020. Forecasting lead times range from 1 to 12 days. DAGCRN consistently outperforms LSTM, BiLSTM, ConvLSTM, and TCN. For 12-day prediction, RMSE is reduced by 36.0%, 24.4%, 13.0%, and 5.5%, respectively, demonstrating its effectiveness in modeling spatiotemporal dependencies for global SSS forecasting. |
| format | Article |
| id | doaj-art-e7d290247a484d3983bcf5c6dd172c5e |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e7d290247a484d3983bcf5c6dd172c5e2025-08-25T11:31:37ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2548008A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinityGuangwen Peng0Yingbing Liu1Cong Xiao2Wenying Du3Changjiang Xiao4School of Computer Science and Technology, Hainan University, Haikou, Hainan, People's Republic of ChinaSchool of Computer Science and Technology, Hainan University, Haikou, Hainan, People's Republic of ChinaSchool of Computer Science and Technology, Hainan University, Haikou, Hainan, People's Republic of ChinaNational Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, People's Republic of ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, People's Republic of ChinaAccurate mid-short term prediction of sea surface salinity (SSS) is essential for operational ocean monitoring, particularly for capturing short-term salinity fluctuations that affect regional ocean dynamics and weather conditions. However, existing models struggle to extract complex spatiotemporal dependencies and are often limited to local regions, reducing their global applicability. To address these challenges, we propose a Dynamic Adaptive Graph Convolutional Recurrent Network (DAGCRN) for global SSS prediction. The DAGCRN employs an encoder–decoder architecture, where both the encoder and decoder integrate adaptive graph convolutional recurrent units (AGCRUs) and gated recurrent units (GRUs). AGCRUs dynamically construct topological relationships via graph convolution to model spatial variations, while GRUs capture temporal dependencies. This enables DAGCRN to effectively model the nonlinear and dynamic nature of global SSS variations. We evaluate DAGCRN's performance on the ESA Sea Surface Salinity CCI v3.21 dataset, which provides global gridded SSS observations from February 2010 to September 2020. Forecasting lead times range from 1 to 12 days. DAGCRN consistently outperforms LSTM, BiLSTM, ConvLSTM, and TCN. For 12-day prediction, RMSE is reduced by 36.0%, 24.4%, 13.0%, and 5.5%, respectively, demonstrating its effectiveness in modeling spatiotemporal dependencies for global SSS forecasting.https://www.tandfonline.com/doi/10.1080/17538947.2025.2548008Sea surface salinitydynamic adaptive graph convolutionglobal mid-short term predictionspatiotemporal prediction |
| spellingShingle | Guangwen Peng Yingbing Liu Cong Xiao Wenying Du Changjiang Xiao A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity International Journal of Digital Earth Sea surface salinity dynamic adaptive graph convolution global mid-short term prediction spatiotemporal prediction |
| title | A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity |
| title_full | A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity |
| title_fullStr | A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity |
| title_full_unstemmed | A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity |
| title_short | A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity |
| title_sort | dynamic adaptive graph convolutional recurrent network model for efficient mid short term prediction of global sea surface salinity |
| topic | Sea surface salinity dynamic adaptive graph convolution global mid-short term prediction spatiotemporal prediction |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2548008 |
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