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

TL;DR

This study evaluates machine learning algorithms, specifically SVMs and MLPs, to predict whether solar flares will be associated with CMEs and SEPs, achieving high accuracy within a 96-hour forecast window.

Contribution

The paper compares the performance of SVMs and MLPs in forecasting solar eruptive events, identifying optimal time frames and demonstrating the effectiveness of machine learning in space weather prediction.

Findings

SVMs outperform MLPs in skill scores.

96-hour forecast yields highest prediction accuracy.

Maximum accuracy achieved for 36 and 108-hour windows.

Abstract

Among the eruptive activity phenomena observed on the Sun, the most technology threatening ones 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 (DONKI) of NASA Space Weather Center, {\it the Geostationary Operational Environmental Satellite} ({\it GOES}), and the Space-Weather Heliospheric and Magnetic Imager Active Region Patches (SHARPs). 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 hours provides the best results in predicting if a flare will be associated with CMEs and SEPs (TSS=0.92$\pm$0.09 and HSS=0.92$\pm$0.08). Additionally, we obtain the maximum TSS and HSS values of 0.91$\pm$0.06 for predicting that a flare will not be associated with CMEs and SEPs for the 36 hour forecast window, while the 108 hour forecast window give the maximum TSS and HSS values for predicting CMEs will not be accompanying any events (TSS=HSS=0.98$\pm$0.02).