Research Progress in Solar Flare Prediction Methods

Abstract Solar flares are one of the strongest outbursts of solar activity, posing a serious threat to Earth’s critical infrastructure, such as communications, navigation, power, and aviation. Therefore, it is essential to accurately predict solar flares in order to ensure the safety of human activities. Currently, the research focuses on two directions: first, identifying predictors with more physical information and higher prediction accuracy, and second, building flare prediction models that can effectively handle complex observational data. In terms of flare observability and predictability, this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in prediction. In flare prediction models, the paper focuses on data-driven models and physical models, with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional data. By reviewing existing traditional machine learning, deep learning, and fusion methods, the key roles of these techniques in improving prediction accuracy and efficiency are revealed. Regarding prevailing challenges, this study discusses the main challenges currently faced in solar flare prediction, such as the complexity of flare samples, the multimodality of observational data, and the interpretability of models. The conclusion summarizes these findings and proposes future research directions and potential technology advancement.