Proc. IAU volume 13, issue S335, P310-313 2017 DOI: 10.1017/s1743921317007293 View full text
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Naoto Nishizuka, Komei Sugiura, Yuki Kubo, Mitsue Den, Shin-ichi Watari, Mamoru Ishii

Abstract: AbstractWe developed a flare prediction model based on the supervised machine learning of solar observation data for 2010-2015. We used vector magnetograms, lower chromospheric brightening, and soft-X-ray data taken by Solar Dynamics Observatory and Geostationary Operational Environmental Satellite. We detected active regions and extracted 60 solar features such as magnetic neutral lines, current helicity, chromospheric brightening, and flare history. We fully shuffled the database and randomly divided it into…

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