Solar flare forecasting based on a Fusion Model

Solar flare prediction is an important subject in the field of space weather. Deep learning technology has greatly promoted the development of this subject. In this study, we propose a novel solar flare forecasting model integrating Deep Residual Network (ResNet) and Support Vector Machine (SVM) for both ≥ C-class (C, M, and X classes) and ≥ M-class (M and X classes) flares. We collected samples of magnetograms from May 1, 2010 to September 13, 2018 from Space-weather Helioseismic and Magnetic Imager (HMI) Active Region Patches and then used a cross-validation method to obtain seven independent data sets. We then utilized five metrics to evaluate our fusion model, based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function. Our results show that the primary metric true skill statistics (TSS) achieves a value of 0.708 ± 0.027 for ≥ C-class prediction, and of 0.758 ± 0.042 for ≥ M-class prediction; these values indicate that our approach performs significantly better than those of previous studies. The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust, suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction. Besides, we also discuss the performance impact of architectural innovation in our fusion model.