Leveraging synthetic data to improve regional sea level predictions
Abstract The rapid increase in sea levels driven by climate change presents serious risks to coastal communities around the globe. Traditional prediction models frequently concentrate on developed regions with extensive tide gauge networks, leaving a significant gap in data and forecasts for develop...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88078-1 |
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| _version_ | 1832571813649973248 |
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| author | Guanchao Tong Jiayou Chao Wenxuan Ma Ziqi Zhong Gaurav Gupta Wei Zhu |
| author_facet | Guanchao Tong Jiayou Chao Wenxuan Ma Ziqi Zhong Gaurav Gupta Wei Zhu |
| author_sort | Guanchao Tong |
| collection | DOAJ |
| description | Abstract The rapid increase in sea levels driven by climate change presents serious risks to coastal communities around the globe. Traditional prediction models frequently concentrate on developed regions with extensive tide gauge networks, leaving a significant gap in data and forecasts for developing countries where the tide gauges are sparse. This study presents a novel deep learning approach that combines TimesGAN with ConvLSTM to enhance regional sea level predictions using the more widely available satellite altimetry data. By generating synthetic training data with TimesGAN, we can significantly improve the predictive accuracy of the ConvLSTM model. Our method is tested across three developed regions—Shanghai, New York, and Lisbon—and three developing regions—Liberia, Gabon, and Somalia. The results reveal that integrating TimesGAN reduces the average mean squared error of the ConvLSTM prediction by approximately 66.1%, 76.6%, 64.5%, 78.2%, 81.7% and 85.1% for Shanghai, New York, Lisbon, Liberia, Gabon, and Somalia, respectively. This underscores the effectiveness of synthetic data in enhancing sea level prediction accuracy, across all regions studied. |
| format | Article |
| id | doaj-art-ad7e20efe88e433fb7275ed24c1212b8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ad7e20efe88e433fb7275ed24c1212b82025-02-02T12:19:37ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-88078-1Leveraging synthetic data to improve regional sea level predictionsGuanchao Tong0Jiayou Chao1Wenxuan Ma2Ziqi Zhong3Gaurav Gupta4Wei Zhu5College of Science, Mathematics and Technology, Wenzhou-Kean UniversityDepartment of Applied Mathematics and Statistics, State University of New York at Stony BrookCollege of Science, Mathematics and Technology, Wenzhou-Kean UniversityCollege of Science, Mathematics and Technology, Wenzhou-Kean UniversityCollege of Science, Mathematics and Technology, Wenzhou-Kean UniversityDepartment of Applied Mathematics and Statistics, State University of New York at Stony BrookAbstract The rapid increase in sea levels driven by climate change presents serious risks to coastal communities around the globe. Traditional prediction models frequently concentrate on developed regions with extensive tide gauge networks, leaving a significant gap in data and forecasts for developing countries where the tide gauges are sparse. This study presents a novel deep learning approach that combines TimesGAN with ConvLSTM to enhance regional sea level predictions using the more widely available satellite altimetry data. By generating synthetic training data with TimesGAN, we can significantly improve the predictive accuracy of the ConvLSTM model. Our method is tested across three developed regions—Shanghai, New York, and Lisbon—and three developing regions—Liberia, Gabon, and Somalia. The results reveal that integrating TimesGAN reduces the average mean squared error of the ConvLSTM prediction by approximately 66.1%, 76.6%, 64.5%, 78.2%, 81.7% and 85.1% for Shanghai, New York, Lisbon, Liberia, Gabon, and Somalia, respectively. This underscores the effectiveness of synthetic data in enhancing sea level prediction accuracy, across all regions studied.https://doi.org/10.1038/s41598-025-88078-1 |
| spellingShingle | Guanchao Tong Jiayou Chao Wenxuan Ma Ziqi Zhong Gaurav Gupta Wei Zhu Leveraging synthetic data to improve regional sea level predictions Scientific Reports |
| title | Leveraging synthetic data to improve regional sea level predictions |
| title_full | Leveraging synthetic data to improve regional sea level predictions |
| title_fullStr | Leveraging synthetic data to improve regional sea level predictions |
| title_full_unstemmed | Leveraging synthetic data to improve regional sea level predictions |
| title_short | Leveraging synthetic data to improve regional sea level predictions |
| title_sort | leveraging synthetic data to improve regional sea level predictions |
| url | https://doi.org/10.1038/s41598-025-88078-1 |
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