The influence of magnetic field parameters and time step on deep learning models of solar flare prediction

The research on solar flare predicting holds significant practical and scientific value for safeguarding human activities. Current solar flare prediction models have not fully considered important factors such as time step length, nor have they conducted a comparative analysis of the physical features in multiple models or explored the consistency in the importance of features. In this work, based on SHARP data from SDO, we build 9 machine learning-based solar flare prediction models for binary “Yes” or “No” class prediction within the next 24 hours, and study the impact of different time steps and other factors on the forecasting performance. The main results are as follows. (1) The predictive performance of eight deep learning models shows an increasing trend as the time step length increases, and the models perform the best at the length of 40. (2) In predicting solar flares of ≥C class and ≥M class, the True Skill Statistic(TSS) of deep learning models consistently outperforms that of baseline model. For the same model, the TSS for predicting ≥M class flares generally exceeds that for predicting ≥C class flares. (3) The Brier Skill Score (BSS) of deep learning models significantly surpasses that of baseline model in predicting ≥C class flares. However, the BSS scores of the nine models are comparable for predicting ≥M class flares. For the same model, the BSS for predicting ≥C class flares is generally higher than that for predicting ≥M class flares. (4) Through feature importance analysis of multiple models, the common features that consistently rank at the top and bottom are identified.