Analysis of the main factors affecting the performance of multi-classification forecast model for solar flares.

Efficient forecasting of solar flares is of significant importance for better risk prevention. Currently, there is relatively rare research on multi/four-classification of flares, and the influence of the number of time steps and data feature dimensions on the prediction performance of multi-class models has not been considered. In this study, we utilize the Space-weather HMI Active Region Patch (SHARP) data to develop two categories of models for multiclass flare prediction within 24 hr, including direct output four-classification models and four-classification models using a cascading scheme. The former encompasses Random Forest (RF) model, Long Short-Term Memory (LSTM) model, and Bidirectional LSTM (BLSTM) model, while the latter includes BLSTM Cascade (BLSTM-C) model and BLSTM Cascade with Attention Mechanism (BLSTM-C-A) model. These two categories of models are employed to contrast the impact of different numbers of time steps and the predictive performance in solar flare multi/four-classification. Additionally, we conduct, for the first time, feature importance analysis for multi/four-classification solar flare prediction using deep learning models. The main results are as follows: (1) As the number of time steps increases, the True Skill Statistic (TSS) scores of the four deep learning models improve, showing an overall upward trend in predictive performance. The models achieve their optimal performance when the number of time steps reaches 120. (2) Among the direct output four-class models, deep learning models (LSTM and BLSTM) outperform traditional machine learning model (RF). In both multi-class and binary-class predictions using deep learning, the BLSTM-C model performs better than other deep learning models (LSTM, BLSTM, and BLSTM-C-A). (3) In the feature importance analysis, the top-ranked important features include SAVNCPP and R_VALUE, while the least important features include SHRGT45 and MEANPOT. [ABSTRACT FROM AUTHOR]