A Novel Sea Surface Temperature Prediction Model Using DBN-SVR and Spatiotemporal Secondary Calibration
Yibo Liu, Zichen Zhao, Zhe Zhang, Yi Yang
Abstract
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.