Enhancing ≥2 MeV Electron Fluence Predictions in GEO Orbit Through Three Deep Learning Strategies

Abstract Forecasting the dynamic variations of high‐energy electrons in geostationary orbit remains a frontier challenge in space physics and is critical for the protection of geostationary satellites. In this study, we develop Transformer‐based models to predict 2 MeV electron daily fluences and explore three distinct data‐centric enhancement strategies to improve forecast accuracy: data augmentation (using Generative Adversarial Network and VAE networks), pre‐training on higher‐resolution data, and Few‐shot Learning (FSL). Using training data from 2012 to 2015 and testing data from 2016 to 2018 from the GOES‐15 satellite, the baseline Transformer model without enhancement achieved prediction efficiency (PE) values of 0.877 and 0.885 for the optimal two‐ and three‐parameter inputs. Data augmentation provided only marginal gains, likely due to the inability of synthetic data to fully capture the complex physical variability of real observations. In contrast, pre‐training yielded improved performance, with PE values of 0.912 and 0.906 for the best two‐ and three‐parameter combinations. FSL proved to be the most effective enhancement strategy, achieving PE of 0.925 or 0.918 with ( F , Vsw , Kp ) or ( F , AE ) as inputs ( F denotes the log 10 of the 2 MeV electron daily fluence) and significantly boosting performance during relativistic electron enhancement events. It successfully predicts 96.9% of days with these events and issues first‐day alerts for 41 out of 66 multi‐day events. Comparative analyses with previous models confirm that the proposed enhancement strategies, particularly FSL and pre‐training, offer substantial gains in forecasting accuracy for 2 MeV electron fluences, providing solutions for data‐limited scenarios in space weather prediction.