A Novel Sea Surface Temperature Prediction Model Using DBN-SVR and Spatiotemporal Secondary Calibration
Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature extr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/10/1681 |
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| Summary: | Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature extraction of a deep belief network (DBN) with the high-precision regression of support vector regression (SVR), enhanced by spatiotemporal secondary calibration (SSC) to better capture SST variation patterns. Using satellite-derived remote sensing data, the DBN-SVR model outperforms baseline methods in both the Indian Ocean and North Pacific regions, demonstrating strong applicability across diverse marine environments. This work advances long-term SST prediction capabilities, providing a reliable foundation for extended-range marine forecasts. |
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| ISSN: | 2072-4292 |