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Solar Flare Prediction Using Machine Learning with Multiwavelength Observations

Published online by Cambridge University Press:  24 July 2018

Naoto Nishizuka ,
Komei Sugiura ,
Yuki Kubo ,
Mitsue Den ,
Shin-ichi Watari  and
Mamoru Ishii
Naoto Nishizuka
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
Komei Sugiura
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
Yuki Kubo
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
Mitsue Den
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
Shin-ichi Watari
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
Mamoru Ishii
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan184-8795 email: [email protected]
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Abstract

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We 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 two for training and testing. To predict the maximum size of flares occurring in the following 24 hours, we used three machine-learning algorithms independently: the support vector machine, the k nearest neighbors (kNN), and the extremely randomized trees. We achieved a skill score (TSS) of greater than 0.9 for kNN. Furthermore, we compared the prediction results in a more operational setting by shuffling and dividing the database with a week unit. It was found that the prediction score depends on the way the database is prepared.

Keywords

Sun: flaresmagnetic fieldssunspotsmethods: statisticaltechniquesimage processing
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 13 , Symposium S335: Space Weather of the Heliosphere: Processes and Forecasts , July 2017 , pp. 310 - 313
Copyright
Copyright © International Astronomical Union 2018 

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