A Deep Leaming Model with Multi-Scale Skip Connections for Solar Flare Prediction Combined with Prior Information

Abstract

Solar flare prediction has been drawing increasing attention due to its impact on the global environment. However, its prediction remains computationally challenging. To address this, we develop a solar flare prediction model using Long Short-Term Memory(LSTM) and Deep Neural Network (DNN) with multi-scale skip connections, referred to as LSTM-DNN Flare Net (LDFN). Different from the existing models, LDFN is constructed based on the physical meanings of variables, which is in accordance with the human cognitive process and thus enhances the interpretability of the model. In order to improve training efficiency, we integrate both long and short skip connections in the construction of the DNN subnet. The experimental results demonstrate that LDFN can outperform traditional machine learning models and typical deep learning models in terms of internationally recognized standard metrics. Furthermore, the trained LDFN is used to rank the importance of top 10 variables affecting solar flares. Results reveal that total unsigned vertical current, fraction of area with shear > 45 and total unsigned flux around high gradient polarity inversion lines are the most informative variables for predicting solar flare, which is consistent with the results of current research.