Prediction of Flares within 10 Days before They Occur on the Sun

Abstract

Abstract Prediction of solar flares due to the effects on Earth and satellites is an important topic for scientists. We develop a method and a tool for flare prediction by applying the support vector machine classifier to unique and independent Zernike moments extracted from active region (AR) images. In the analysis, we used the Helioseismic and Magnetic Imager (HMI) line-of-sight magnetograms, the Atmospheric Imaging Assembly (AIA) ultraviolet (UV at 1600 Å) and extreme ultraviolet (EUV at 304, 171, 193, 211, 335, 94, and 131 Å) images for a period of eight years of the solar cycle 24 (2010 June to 2018 September). The power-law behavior for the frequency distribution of the large flaring time window—the time interval between the occurrence of an AR and first large flare (X- and M-class) therein—indicated that most of the large flares appeared within 150 hr. The True Skill Score (TSS) metric for the performance of the win classifier that (uses the outputs of the HMI and AIA at 193, 211, 94, and 131 Å classifiers) was obtained as 0.86 ± 0.04. We also showed that the maximum value of the TSS for prediction of large flares for the win classifiers was about 0.95 ± 0.03 on the flaring day and decreased to 0.76 ± 0.1 within 4 to 10 days before flaring.