Skip to main content Accessibility help
×
Home

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations

  • Naoto Nishizuka (a1), Komei Sugiura (a1), Yuki Kubo (a1), Mitsue Den (a1), Shin-ichi Watari (a1) and Mamoru Ishii (a1)...

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.

    • 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
      ×

Copyright

References

Hide All
Ahmed, O. W., Qahwaji, R., Colak, T., et al. 2013, Sol. Phys., 283, 157
Barnes, G., Leka, K. D., Schrijver, C. J., et al. 2016, Astrophys. J., 829, 89
Bloomfield, D. S., Higgins, P. A., McAteer, R. T. J. & Gallagher, P. 2012, ApJ(Letters), 747, L41
Bobra, M. G. & Couvidat, S. 2015, Astrophys. J., 798, 135
Cortes, C. & Vapnik, V. 1995, Mach. Learn., 20, 273
Crown, M. D. 2012, Space Weather, 10, S06006
Dasarathy, B. V. 1991, Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques (Los Alamitos, CA: IEEE Computer Society Press), 447
Devos, A., Verbeeck, C. & Robbrecht, E. 2014, J. Space Weather Space Clim., 4, A29
Guerts, P., Ernst, D. & Wehenkel, L. 2006, Mach. Learn., 63, 3
Hanssen, A. J. & Kuipers, W. J. 1965, Meded Verhand, 81, 2
Kubo, Y., Den, M. & Ishii, M. 2017, J. Space Weather Space Clim., 7, A20
Leka, K. D. & Barnes, G. 2003, Astrophys. J., 595, 1296
Lemen, J., Title, A. M., Akin, D. J., et al. 2012, Sol. Phys., 275, 17
Liu, C., Deng, N., Wang, J. T. L. & Wang, H. 2017, Astrophys. J., 843, 104
McCloskey, A. E., Gallagher, P. T. & Bloomfield, D. S. 2016, Sol. Phys., 291, 1711
Murray, S. A., Bingham, S., Sharpe, M. & Jackson, D. R. 2017, Space Weather, 15, 577
Nishizuka, N., Sugiura, K., Kubo, Y., et al. 2017, Astrophys. J., 835, 156
Pesnell, W., Thompson, B. J., Chamberlin, P. C., et al. 2012, Sol. Phys., 275, 3
Raboonik, A., Safari, H., Alipour, N. & Wheatland, M. S. 2017, Astrophys. J., 834, 11
Scherrer, P. H., Schou, J., Bush, R. I., et al. 2012, Sol. Phys., 275, 207
MathJax
MathJax is a JavaScript display engine for mathematics. For more information see http://www.mathjax.org.

Keywords

Solar Flare Prediction Using Machine Learning with Multiwavelength Observations

  • Naoto Nishizuka (a1), Komei Sugiura (a1), Yuki Kubo (a1), Mitsue Den (a1), Shin-ichi Watari (a1) and Mamoru Ishii (a1)...

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed