Solar Physics

, Volume 241, Issue 1, pp 195–211 | Cite as

Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations

  • R. QahwajiEmail author
  • T. Colak


In this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use.


Solar Activity Solar Cycle Solar Phys Hide Node Sunspot Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Acir, N., Guzelis, C.: 2004, Expert. Syst. Appl. 27, 451. CrossRefGoogle Scholar
  2. Benkhalil, A., Zharkova, V., Ipson, S., Zharkov, S.: 2003, In: Holstein, H., Labrosse, F. (eds.) AISB’03 Symposium on Biologically-Inspired Machine Vision, Theory and Application, University of Wales, Aberystwyth. Google Scholar
  3. Bishop, C.M.: 1995, Neural Networks for Pattern Recognition, Oxford University Press, London, p. 164. Google Scholar
  4. 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
  5. Bornmann, P.L., Shaw, D.: 1994, Solar Phys. 150, 127. CrossRefADSGoogle Scholar
  6. Broomhead, D.S., Lowe, D.: 1988, Complex Syst. 2, 321. zbMATHMathSciNetGoogle Scholar
  7. Calvo, R.A., Ceccatto, H.A., Piacentini, R.D.: 1995, Astrophys. J. 444, 916. CrossRefADSGoogle Scholar
  8. Distante, C., Ancona, N., Siciliano, P.: 2003, Sens. Actuators B: Chem. 88, 30. CrossRefGoogle Scholar
  9. Fahlmann, S.E., Lebiere, C.: 1989, In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems 2 (NIPS-2), Morgan Kaufmann, Denver, p. 524. Google Scholar
  10. Frank, R.J., Davey, N., Hunt, S.P.: 1997, J. Intell. Robot. Syst. 31, 91. CrossRefGoogle Scholar
  11. Fukunaga, K.: 1990, Introduction to Statistical Pattern Recognition, Academic, New York, p. 220. zbMATHGoogle Scholar
  12. Gallagher, P.T., Moon, Y.J., Wang, H.M.: 2002, Solar Phys. 209, 171. CrossRefADSGoogle Scholar
  13. Gao, J.L., Wang, H.M., Zhou, M.C.: 2002, Solar Phys. 205, 93. CrossRefADSGoogle Scholar
  14. Greatrix, G.R., Curtis, G.H.: 1973, Observatory 93, 114. ADSGoogle Scholar
  15. Hale, G.E., Ellerman, F., Nicholson, S.B., Joy, A.H.: 1919, Astrophys. J. 49, 153. CrossRefADSGoogle Scholar
  16. Hathaway, D., Wilson, R.M., Reichmann, E.J.: 1994, Solar Phys. 151, 177. CrossRefADSGoogle Scholar
  17. Huang, Z., Chen, H.C., Hsu, C.J., Chen, W.H., Wu, S.S.: 2004, Decis. Support Syst. 37, 543. CrossRefGoogle Scholar
  18. Koskinen, H., Eliasson, L., Holback, B., Andersson, L., Eriksson, A., Mälkki, A., Norberg, O., Pulkkinen, T., Viljanen, A., Wahlund, J.-E., Wu, J.-G.: 1999, Space Weather and Interactions with Spacecraft, Finnish Meteorological Institute Reports 1999-4. Google Scholar
  19. Künzel, H.: 1960, Astron. Nachr. 285, 271. ADSGoogle Scholar
  20. Lantos, P.: 2006, Solar Phys. 236, 199. CrossRefADSGoogle Scholar
  21. Lefebvre, S., Rozelot, J.P.: 2004, Solar Phys. 219, 25. CrossRefADSGoogle Scholar
  22. Liu, C., Deng, N., Liu, Y., Falconer, D., Goode, P.R., Denker, C., Wang, H.M.: 2005, Astrophys. J. 622, 722. CrossRefADSGoogle Scholar
  23. Manik, A., Singh, A., Yan, S.: 2004, In: Berkowitz, E.G. (ed.) Fifteenth Midwest Artificial Intelligence and Cognitive Sciences Conference, Omnipress, Chicago, p. 74. Google Scholar
  24. McIntosh, P.S.: 1990, Solar Phys. 125, 251. CrossRefADSGoogle Scholar
  25. Pal, M., Mather, P.M.: 2004, Futur. Gener. Comput. Syst. 20, 1215. CrossRefGoogle Scholar
  26. Pap, J., Bouwer, S., Tobiska, W.: 1990, Solar Phys. 129, 165. CrossRefADSGoogle Scholar
  27. Qahwaji, R., Colak, T.: 2006a, Int. J. Comput. Appl. 13, 9. Google Scholar
  28. Qahwaji, R., Colak, T.: 2006b, In: Chu, H.W., Aguilar, J., Rishe, N., Azoulay, J. (eds.) The Third International Conference on Cybernetics and Information Technologies, Systems and Applications, International Institute of Informatics and Systemics, Orlando, p. 192. Google Scholar
  29. Qu, M., Shih, F.Y., Jing, J., Wang, H.M.: 2003, Solar Phys. 217, 157. CrossRefADSGoogle Scholar
  30. Sakurai, K.: 1970, Planet. Space Sci. 18, 33. CrossRefADSGoogle Scholar
  31. Sammis, I., Tang, F., Zirin, H.: 2000, Astrophys. J. 540, 583. CrossRefADSGoogle Scholar
  32. Schetinin, V.: 2003, Neural Process. Lett. 17, 21. CrossRefGoogle Scholar
  33. Shet, R.N., Lai, K.H., Edirisinghe, E., Chung, P.W.H.: 2005, In: Marques, J.S., de la Pe’rez, B.N., Pina, P. (eds.) Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, vol. 3523, Springer, Berlin, p. 343. Google Scholar
  34. Shi, Z.X., Wang, J.X.: 1994, Solar Phys. 149, 105. CrossRefADSGoogle Scholar
  35. Shih, F.Y., Kowalski, A.J.: 2003, Solar Phys. 218, 99. CrossRefADSGoogle Scholar
  36. Smieja, F.J.: 1993, Circuits Syst. Signal Process. 12, 331. zbMATHCrossRefGoogle Scholar
  37. Sutton, R.S., Barto, A.G.: 1998, Reinforcement Learning: An Introduction, MIT, Cambridge, p. 193. Google Scholar
  38. Ternullo, M., Contarino, L., Romano, P., Zuccarello, F.: 2006, Astron. Nachr. 327, 36. CrossRefADSGoogle Scholar
  39. Turmon, M., Pap, J.M., Mukhtar, S.: 2002, Astrophys. J. 568, 396. CrossRefADSGoogle Scholar
  40. Vapnik, V.: 1999, The Nature of Statistical Learning Theory, Springer, New York, p. 138. Google Scholar
  41. Veronig, A., Temmer, M., Hanslmeier, A., Otruba, W., Messerotti, M.: 2002, Astron. Astrophys. 382, 1070. CrossRefADSGoogle Scholar
  42. Wang, H., Qu, M., Shih, F., Denker, C., Gerbessiotis, A., Lofdahl, M., Rees, D., Keller, C.: 2003, Bull. Am. Astron. Soc. 36, 755. ADSCrossRefGoogle Scholar
  43. Warwick, C.S.: 1966, Astrophys. J. 145, 215. CrossRefADSGoogle Scholar
  44. Wheatland, M.S.: 2004, Astrophys. J. 609, 1134. CrossRefADSGoogle Scholar
  45. Zharkova, V., Ipson, S., Benkhalil, A., Zharkov, S.: 2005, Artif. Intell. Rev. 23, 209. CrossRefGoogle Scholar
  46. Zharkova, V., Schetinin, V.: 2003, In: Jain, L.C., Howlett, R.J., Palade, V. (eds.) Proceedings of the Seventh International Conference on Knowledge-Based Intelligent Information and Engineering Systems KES’03, Springer, Oxford, p. 148. Google Scholar
  47. Zirin, H., Liggett, M.A.: 1987, Solar Phys. 113, 267. CrossRefADSGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

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

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