Using Machine Learning Methods to Forecast if Solar Flares Will Be Associated with CMEs and SEPs

Abstract

Among the eruptive activity phenomena observed on the Sun, those that threaten human technology the most are flares with associated coronal mass ejections (CMEs) and solar energetic particles (SEPs). Flares with associated CMEs and SEPs are produced by magnetohydrodynamical processes in magnetically active regions (ARs) on the Sun. However, these ARs do not only produce flares with associated CMEs and SEPs, they also lead to flares and CMEs, which are not associated with any other event. In an attempt to distinguish flares with associated CMEs and SEPs from flares and CMEs, which are unassociated with any other event, we investigate the performances of two machine learning algorithms. To achieve this objective, we employ support vector machines (SVMs) and multilayer perceptrons (MLPs) using data from the Space Weather Database of Notification, Knowledge, Information of NASA Space Weather Center, the Geostationary Operational Environmental Satellite , and the Space-Weather Heliospheric and Magnetic Imager Active Region Patches. We show that True Skill Statistics (TSS) and Heidke Skill Scores (HSS) calculated for SVMs are slightly better than those from the MLPs. We also show that the forecasting time frame of 96 hr provides the best results in predicting if a flare will be associated with CMEs and SEPs (TSS = 0.92 ± 0.09 and HSS = 0.92 ± 0.08). Additionally, we obtain the maximum TSS and HSS values of 0.91 ± 0.06 for predicting that a flare will not be associated with CMEs and SEPs for the 36 hr forecast window, while the 108 hr forecast window gives the maximum TSS and HSS values for predicting that CMEs will not be accompanying any events (TSS = HSS = 0.98 ± 0.02).