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Title: Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

Abstract

We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed thatmore » previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.« less

Authors:
; ; ; ;  [1];  [2]
  1. Applied Electromagnetic Research Institute, National Institute of Information and Communications Technology, 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795 (Japan)
  2. Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology (Japan)
Publication Date:
OSTI Identifier:
22663944
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 835; Journal Issue: 2; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; CHROMOSPHERE; COMPARATIVE EVALUATIONS; EMISSION; FORECASTING; GAMMA RADIATION; GOES SATELLITES; HELICITY; MAGNETIC FIELDS; MAGNETIC FLUX; RANDOMNESS; SOFT X RADIATION; SOLAR FLARES; STATISTICS; SUN; ULTRAVIOLET RADIATION

Citation Formats

Nishizuka, N., Kubo, Y., Den, M., Watari, S., Ishii, M., and Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp. Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms. United States: N. p., 2017. Web. doi:10.3847/1538-4357/835/2/156.
Nishizuka, N., Kubo, Y., Den, M., Watari, S., Ishii, M., & Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp. Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms. United States. doi:10.3847/1538-4357/835/2/156.
Nishizuka, N., Kubo, Y., Den, M., Watari, S., Ishii, M., and Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp. Wed . "Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms". United States. doi:10.3847/1538-4357/835/2/156.
@article{osti_22663944,
title = {Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms},
author = {Nishizuka, N. and Kubo, Y. and Den, M. and Watari, S. and Ishii, M. and Sugiura, K., E-mail: nishizuka.naoto@nict.go.jp},
abstractNote = {We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.},
doi = {10.3847/1538-4357/835/2/156},
journal = {Astrophysical Journal},
number = 2,
volume = 835,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}