Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
  • Home
EnglishFrançais
Proceedings of the International Astronomical Union Proceedings of the International Astronomical Union

Article contents

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, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Komei Sugiura
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Yuki Kubo
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Mitsue Den
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Shin-ichi Watari
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Mamoru Ishii
Affiliation:
National Institute of Information and Communications Technology, 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, Japan 184-8795 email: nishizuka.naoto@nict.go.jp
Corresponding
E-mail address:
nishizuka.naoto@nict.go.jp
Save pdf (0.57 mb)
Rights & Permissions[Opens in a new window]

Abstract

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: flares magnetic fields sunspots methods: statistical techniques image 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 

References

Ahmed, O. W., Qahwaji, R., Colak, T., et al. 2013, Sol. Phys., 283, 157CrossRefGoogle Scholar
Barnes, G., Leka, K. D., Schrijver, C. J., et al. 2016, Astrophys. J., 829, 89CrossRefGoogle Scholar
Bloomfield, D. S., Higgins, P. A., McAteer, R. T. J. & Gallagher, P. 2012, ApJ(Letters), 747, L41CrossRefGoogle Scholar
Bobra, M. G. & Couvidat, S. 2015, Astrophys. J., 798, 135CrossRefGoogle Scholar
Cortes, C. & Vapnik, V. 1995, Mach. Learn., 20, 273Google Scholar
Crown, M. D. 2012, Space Weather, 10, S06006CrossRefGoogle Scholar
Dasarathy, B. V. 1991, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (Los Alamitos, CA: IEEE Computer Society Press), 447Google Scholar
Devos, A., Verbeeck, C. & Robbrecht, E. 2014, J. Space Weather Space Clim., 4, A29CrossRefGoogle Scholar
Guerts, P., Ernst, D. & Wehenkel, L. 2006, Mach. Learn., 63, 3CrossRefGoogle Scholar
Hanssen, A. J. & Kuipers, W. J. 1965, Meded Verhand, 81, 2Google Scholar
Kubo, Y., Den, M. & Ishii, M. 2017, J. Space Weather Space Clim., 7, A20CrossRefGoogle Scholar
Leka, K. D. & Barnes, G. 2003, Astrophys. J., 595, 1296CrossRefGoogle Scholar
Lemen, J., Title, A. M., Akin, D. J., et al. 2012, Sol. Phys., 275, 17CrossRefGoogle Scholar
Liu, C., Deng, N., Wang, J. T. L. & Wang, H. 2017, Astrophys. J., 843, 104CrossRefGoogle Scholar
McCloskey, A. E., Gallagher, P. T. & Bloomfield, D. S. 2016, Sol. Phys., 291, 1711CrossRefGoogle Scholar
Murray, S. A., Bingham, S., Sharpe, M. & Jackson, D. R. 2017, Space Weather, 15, 577CrossRefGoogle Scholar
Nishizuka, N., Sugiura, K., Kubo, Y., et al. 2017, Astrophys. J., 835, 156CrossRefGoogle Scholar
Pesnell, W., Thompson, B. J., Chamberlin, P. C., et al. 2012, Sol. Phys., 275, 3CrossRefGoogle Scholar
Raboonik, A., Safari, H., Alipour, N. & Wheatland, M. S. 2017, Astrophys. J., 834, 11CrossRefGoogle Scholar
Scherrer, P. H., Schou, J., Bush, R. I., et al. 2012, Sol. Phys., 275, 207CrossRefGoogle Scholar

Full text views

Full text views reflects PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.

Total number of HTML views: 0
Total number of PDF views: 50 *
View data table for this chart

* Views captured on Cambridge Core between 24th July 2018 - 26th January 2021. This data will be updated every 24 hours.

Access
1 Cited by
Hostname: page-component-898fc554b-54xgk Total loading time: 0.9 Render date: 2021-01-26T14:26:56.498Z Query parameters: { "hasAccess": "1", "openAccess": "0", "isLogged": "0", "lang": "en" } Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": false, "newCiteModal": false }

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations
Available formats
×
×

Reply to: Submit a response

Your details

Conflicting interests

Do you have any conflicting interests? *