Skip to main content

Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm

Abstract

In order to enhance the reproduction of the recovery phase Dst index of a geomagnetic storm which has been shown by previous studies to be poorly reproduced when compared with the initial and main phases, an artificial neural network with one hidden layer and error back-propagation learning has been developed. Three hourly Dst values before the minimum Dst in the main phase in addition to solar wind data of IMF southward-component Bs, the total strength Bt and the square root of the dynamic pressure, \(sqrt {n{V^2}}\), for the minimum Dst, i.e., information on the main phase was used to train the network. Twenty carefully selected storms from 1972–1982 were used for the training, and the performance of the trained network was then tested with three storms of different Dst strengths outside the training data set. Extremely good agreement between the measured Dst and the modeled Dst has been obtained for the recovery phase. The correlation coefficient between the predicted and observed Dst is more than 0.95. The average relative variance is 0.1 or less, which means that more than 90% of the observed Dst variance is predictable in our model. Our neural network model suggests that the minimum Dst of a storm is significant in the storm recovery process.

References

  1. Baker, D. N., Statistical analyses in the study of solar wind-magnetosphere coupling, in Solar Wind-Magnetosphere Coupling, edited by Y. Kamide and J. A. Slavin, pp. 17–38, Terra Scientific Pub., Tokyo, 1986.

    Google Scholar 

  2. Burton, R. K., R. L. McPherron, and C. T. Russell, An empirical relationship between interplanetary conditions and Dst, J. Geophys. Res., 80, 4204–4214, 1975.

    Article  Google Scholar 

  3. Clauer, C. R., The technique of linear prediction filters applied to studies of solar wind-magnetosphere coupling, in Solar Wind-Magnetosphere Coupling, edited by Y. Kamide and J. A. Slavin, pp. 39–57, Terra Scientific Pub., Tokyo, 1986.

    Google Scholar 

  4. Daglis, I. A., The role of magnetosphere-ionosphere coupling in magnetic storm dynamics, in Magnetic Storms, edited by B. T. Tsurutani, W. D. Gonzalez, Y. Kamide, and J. K. Arballo, pp. 107–116, AGU, Washington, D.C., 1997.

    Google Scholar 

  5. Detman, T. R. and D. Vassiliadis, Review of techniques for magnetic storm forecasting, in Magnetic Storms, edited by B. T. Tsurutani, W. D. Gonzalez, Y. Kamide, and J. K. Arballo, pp. 253–266, AGU, Washington, D.C., 1997.

    Google Scholar 

  6. Fausett, L., Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, 461 pp., Prentice Hall, Englewood Cliffs, NJ 07632, 1994.

    Google Scholar 

  7. Fok, M.-C., J. U. Kozyra, A. F. Nagy, and T. E. Cravens, Lifetime of ring current particles due to Coulomb collisions in the plasmasphere, J. Geophys. Res., 96, 7861–7867, 1991.

    Article  Google Scholar 

  8. Freeman, J., A. Nagai, P. Reiff, W. Denig, S. Gussenhoven, M. A. Shea, M. Heinemann, F. Rich, and M. Hairston, The use of neural networks to predict magnetospheric parameters for input to a magnetospheric forecast model, in Proceedings of the International Workshop on Artificial Intelligence Applications in Solar Terrestrial Physics, edited by J. Joselyn, H. Lundstedt, and J. Trolinger, pp. 167–182, Lund, Sweden, 1993.

    Google Scholar 

  9. Gleisner, H. and H. Lundstedt, Response of the auroral electrojets to the solar wind modeled with neural networks, J. Geophys. Res., 102, 14269–14278, 1997.

    Article  Google Scholar 

  10. Gleisner, H., H. Lundstedt, and P. Wintoft, Predicting geomagnetic storms from solar wind data using time-delay neural networks, Ann. Geophys., 14, 679–686, 1996.

    Article  Google Scholar 

  11. Gloeckler, G. and D. C. Hamilton, AMPTEE ion composition results, Phys. Scr., T18, 73–84, 1987.

    Article  Google Scholar 

  12. Hamilton, D. C., 5. Storm dynamics/ring current, in Magnetic Storms, edited by B. T. Tsurutani, W. D. Gonzalez, Y. Kamide, and J. K. Arballo, pp. 6–7, AGU, Washington, D.C., 1997.

    Google Scholar 

  13. Hamilton, D. C., G. Gloeckler, F. M. Ipavich, W. Studemann, B. Wilken, and G. Kremser, Ring current development during during the great geomagnetic storm of February 1986, J. Geophys. Res., 93, 14343–14355, 1988.

    Article  Google Scholar 

  14. Hertz, J., A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation, lecture notes vol. 1, Santa Fe Institute Studies in the sciences of complexity, 327 pp., Addison-Wesley, Redwood City, CA 94065, 1991.

    Google Scholar 

  15. Ichikawa, H., Layered Neural Networks, 184 pp., Kyoritu Pub., Tokyo, 108, 1993.

    Google Scholar 

  16. Iyemori, T. and H. Maeda, Prediction of geomagnetic activities from solar wind parameters based on the linear prediction theory, in Solar-Terrestrial Predictions Proceedings, Vol. 4, U.S. Dept. of Commerce, Boulder, CO, A–1–A–7, 1980.

    Google Scholar 

  17. Iyemori, T., H. Maeda, and T. Kamei, Impulse response of geomagnetic indices to interplanetary magnetic fields, J. Geomag. Geoelectr., 31, 1–9, 1979.

    Article  Google Scholar 

  18. Kamide, Y., N. Yokoyama, W. Gonzalez, B. T. Tsurutani, I. A. Daglis, A. Brekke, and S. Masuda, Two step development of geomagnetic storms, J. Geophys. Res., 103, 6917–6921, 1998.

    Article  Google Scholar 

  19. Lundstedt, H. and P. Wintoft, Prediction of geomagnetic storms from solarwind data with the use of a neural network, Ann. Geophys., 12, 19–24, 1994.

    Article  Google Scholar 

  20. Ogilvie, K. W., L. F. Burlaga, and T. D. Wilkerson, Plasma observations on Explorer 34, J. Geophys. Res., 73, 6809–6824, 1968.

    Article  Google Scholar 

  21. Pudovkin, M. I., S. A. Zaitseva, and L. Z. Sizova, Growth and decay of magnetospheric ring current, Planet. Space Sci., 33, 1097–1102, 1985.

    Article  Google Scholar 

  22. Rostoker, G. and C.-G. Fälthammar, Relationships between changes in the interplanetary magnetic field and the variations in the magnetic field at the earth’s surface, J. Geophys. Res., 72, 5853–5863, 1967.

    Article  Google Scholar 

  23. Siscoe, G. L., V. Formisano, and A. J. Lazarus, Relation between geomagnetic sudden impulses and solar wind pressure changes—an experimental investigation, J. Geophys. Res., 73, 4869–4874, 1968.

    Article  Google Scholar 

  24. Smith, P. H. and N. K. Bewtra, Charge exchange lifetimes for ring current ions, Space Sci. Rev., 22, 301–318, 1978.

    Article  Google Scholar 

  25. Sugiura, M., Hourly values of equatorial Dst for the IGY, Ann. Int. Geophys. Year, 35, 49, 1964.

    Google Scholar 

  26. Vassiliadis, D., A. J. Klimas, D. N. Baker, and D. A. Roberts, A description of solar wind magnetosphere coupling based on nonlinear filters, J. Geophys. Res., 100, 3495–3512, 1995.

    Article  Google Scholar 

  27. Vassiliadis, D., A. J. Klimas, and D. N. Baker, Nonlinear ARMA models for the Dst index and their physical interpretation, paper presented at the Third International Conference on Substorms (ICS-3), Versailles, France, May 12–17, 1996.

  28. Wu, J.-G. and H. Lundstedt, Prediction of geomagnetic storms from solar wind data using Elman recurrent neural networks, Geophys. Res. Lett., 23, 319–322, 1996.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Samuel Kugblenu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kugblenu, S., Taguchi, S. & Okuzawa, T. Prediction of the geomagnetic storm associated Dst index using an artificial neural network algorithm. Earth Planet Sp 51, 307–313 (1999). https://doi.org/10.1186/BF03352234

Download citation

Keywords

  • Solar Wind
  • Root Mean Square Error
  • Hide Layer
  • Storm Event
  • Recovery Phase