Solar Physics

, Volume 248, Issue 2, pp 471–483 | Cite as

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations

Article

Abstract

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.

Keywords

CMEs prediction Machine learning Solar flares Space weather CME Neural networks Support vector machines 

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References

  1. Acir, N., Guzelis, C.: 2004, Expert Syst. Appl. 27, 451. CrossRefGoogle Scholar
  2. Borda, R.A.F., Mininni, P.D., Mandrini, C.H., Gomez, D.O., Bauer, O.H., Rovira, M.G.: 2002, Solar Phys. 206, 347. CrossRefADSGoogle Scholar
  3. Cliver, E.W., Hudson, H.S.: 2002, J. Atmos. Solar-Terr. Phys. 64, 231. CrossRefADSGoogle Scholar
  4. Colak, T., Qahwaji, R.: 2007, Adv. Soft Comput. 39, 316. CrossRefGoogle Scholar
  5. Distante, C., Ancona, N., Siciliano, P.: 2003, Sens. Actuator B-Chem. 88, 30. CrossRefGoogle Scholar
  6. Fawcett, T.: 2006, Pattern Recognit. Lett. 27, 861. CrossRefGoogle Scholar
  7. Fukunaga, K.: 1990, Introduction to Statistical Pattern Recognition, Academic, New York. MATHGoogle Scholar
  8. Gosling, J.T.: 1995, J. Geophys. Res. 100, 7921. CrossRefADSGoogle Scholar
  9. Huang, Z., Chen, H.C., Hsu, C.J., Chen, W.H., Wu, S.S.: 2004, Decis. Support Syst. 37, 543. CrossRefGoogle Scholar
  10. Koskinen, H., Tanskanen, E., Pirjola, R., Pulkkinen, A., Dyer, C., Rogers, D., Cannon, P., Menville, J., Boscher, D.: 2001, Space Weather Effects Catalogue, Finnish Meteorological Inst., Helsinki. Google Scholar
  11. Kurokawa, H.: 2002, Commun. Res. 49, 49. Google Scholar
  12. Lenz, D.: 2004, Ind. Phys. 9, 18. Google Scholar
  13. Lin, R.P., Hudson, H.S.: 1976, Solar Phys. 50, 153. CrossRefADSGoogle Scholar
  14. Pal, M., Mather, P.M.: 2004, Fut. Gener. Comput. Syst. 20, 1215. CrossRefGoogle Scholar
  15. Qahwaji, R., Colak, T.: 2006, Comput. Appl. 13, 9. Google Scholar
  16. Qahwaji, R., Colak, T.: 2007, Solar Phys. 241, 195. CrossRefADSGoogle Scholar
  17. Qu, M., Shih, F.Y., Jing, J., Wang, H.M.: 2003, Solar Phys. 217, 157. CrossRefADSGoogle Scholar
  18. Tousey, R.: 1973, Adv. Space Res. 13, 713. Google Scholar
  19. Webb, D.F.: 2000, J. Atmos. Solar-Terr. Phys. 62, 1415. CrossRefADSGoogle Scholar
  20. Wilson, R.M., Hildner, E.: 1984, Solar Phys. 91, 169. CrossRefADSGoogle Scholar
  21. Yashiro, S., Gopalswamy, N., Akiyama, S., Michalek, G., Howard, R.A.: 2005, J. Geophys. Res. 110, A12S05. CrossRefGoogle Scholar
  22. Yashiro, S., Gopalswamy, N., Akiyama, S., Howard, R.A.: 2006, In: 36th COSPAR Scientific Assembly, Beijing, China, 1778. Google Scholar
  23. Yun-Chun, J., Le-Ping, L., Li-Heng, Y.: 2006, Chin. J. Astron. Astrophys. 6, 345. CrossRefADSGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Department of Electronic Imaging and Media CommunicationsUniversity of BradfordBradfordEngland, UK

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