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Horizon of Neural Network Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth

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Published:25 September 2015Publication History

ABSTRACT

The difficulty of prediction of the time series of relativistic electrons flux in the outer radiation belt of the Earth is caused by the complexity and nonlinearity of the magnetosphere of the Earth as a dynamic system, and by the properties of data obtained from space experiments. This study considers different approaches to neural network prediction of the values of relativistic electrons flux in the outer radiation belt of the Earth by the parameters of solar wind measured at the Earth's orbit and by the values of geomagnetic indices. Comparison of quality indices of predictions with horizon from one to twelve hours among each other and with predictions of trivial models is performed.

References

  1. Advanced Composition Explorer (ACE) Project, http://www.srl.caltech.edu/ACE/Google ScholarGoogle Scholar
  2. Baker, D.N., McPherron, R.L., Cayton, T.E., and Klebesadel, R.W. 1990. Linear prediction filter analysis of relativistic electron properties at 6.6 RE. J. Geophys. Res. 95 (A9), 15133--15140. DOI= http://dx.doi.org/10.1029/JA095iA09p15133Google ScholarGoogle Scholar
  3. Friedel, R.H., Reeves W.G.P., and Obara, T. 2002. Relativistic electron dynamics in the inner magnetosphere -- A review. J. Atmos. Sol.-Terr. Phy. 64, 265--283. DOI= http://dx.doi.org/10.1016/S1364-6826(01)00088-8Google ScholarGoogle ScholarCross RefCross Ref
  4. Fukata, M., Taguchi, S., Okuzawa, T., and Obara, T. 2002. Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms. Ann. Geophys. 20 (7), 947--951. DOI= http://dx.doi.org/10.5194/angeo-20-947-2002Google ScholarGoogle ScholarCross RefCross Ref
  5. Geomagnetic Data Service in Kyoto, http://wdc.kugi.kyoto-u.ac.jp/wdc/Sec3.htmlGoogle ScholarGoogle Scholar
  6. Geostationary Operational Environmental Satellite Project, http://rsd.gsfc.nasa.gov/goes/Google ScholarGoogle Scholar
  7. Geostationary Operational Environmental Satellite Project in Space Weather Prediction Center, http://www.swpc.noaa.gov/ftpdir/lists/pchan/READMEGoogle ScholarGoogle Scholar
  8. Iucci, N., Levitin, A.E., Belov, A.V. et al. 2005. Space weather conditions and spacecraft anomalies in different orbits. Adv. Space Res. (Space Weather) 3 (1), S01001. DOI= http://dx.doi.org/10.1029/2003SW000056Google ScholarGoogle Scholar
  9. Kataoka, R. and Miyoshi, Y. 2008. Average profiles of the solar wind and outer radiation belt during the extreme flux enhancement of relativistic electrons at geosynchronous orbit. Ann. Geophys. 26, 1335--1339. DOI= http://dx.doi.org/10.5194/angeo-26-1335-2008Google ScholarGoogle ScholarCross RefCross Ref
  10. Koons, H.C. and Gorney, D.J. 1990. A neural network model of the relativistic electron flux at geosynchronous orbit. J. Geophys. Res. 96, 5549--5556. DOI= http://dx.doi.org/10.1029/90JA02380Google ScholarGoogle ScholarCross RefCross Ref
  11. Kuznetsov, S.N. and Tverskaya, L.V. 2007. Physical Conditions in Open Space: Radiation. In: Model of Cosmos, Panasyuk, M.I. and Novikov L.S. (eds.), Vol. 1, chapter 3.4, 518--546. Universitet, Knizhnyi dom, Moscow. (In Russian.) ISBN= 978-5-98227-419-9.Google ScholarGoogle Scholar
  12. Ling, A. G., Ginet, G. P., Hilmer, R. V., and Perry, K. L. 2010. A neural network-based geosynchronous relativistic electron flux forecasting model. Adv. Space Res. (Space Weather) 8 (9), S09003. DOI= http://dx.doi.org/10.1029/2010SW000576Google ScholarGoogle Scholar
  13. Pauliukas, G.A. and Blake, J.B. 1979. Effects of the solar wind on magnetospheric dynamics: Energetic electrons at the synchronous orbit. In: Quantitative Modeling of Magnetospheric Processes, Olson, W.P. (ed.). Geophys. Monogr. Ser. AGU, Washington D.C. 21, 180--202.Google ScholarGoogle Scholar
  14. Peters, E. 1994. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley, 336 pp. ISBN= 978-0-471-58524-4.Google ScholarGoogle Scholar
  15. Reeves, G.D., McAdams K.L., Friedel, R.H.W. et al. 2003. Acceleration and loss of relativistic electrons during geomagnetic storms. Geophys. Res. Lett. 30 (10), 1529. DOI= http://dx.doi.org/10.1029/2002GL016513.Google ScholarGoogle ScholarCross RefCross Ref
  16. Relativistic Electron Forecast Model of Space Weather Prediction Center, http://www.swpc.noaa.gov/refm/Google ScholarGoogle Scholar
  17. Romanova, N.V., Pilipenko, V.A., Yagova, N.V., and Belov, A.V. 2005. Statistical Correlation of the Rate of Failures on Geosynchronous Satellites with Fluxes of Energetic Electrons and Protons. Cosmic Res+ 43 (3), 179--185. DOI= http://dx.doi.org/10.1007/s10604-005-0032-6.Google ScholarGoogle Scholar
  18. Space Physics Interactive Data Resource -- SPIDR, http://spidr.ngdc.noaa.gov/spidr/Google ScholarGoogle Scholar
  19. Space Weather Prediction Center, http://www.swpc.noaa.govGoogle ScholarGoogle Scholar
  20. Turner, D.L., Shprits, Y., Hartinger, M., and Angelopoulos, V. 2012. Explaining sudden losses of outer radiation belt electrons during geomagnetic storms. Natural Physics 8, 208--212. DOI= http://dx.doi.org/10.1038/nphys2185.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Horizon of Neural Network Prediction of Relativistic Electrons Flux in the Outer Radiation Belt of the Earth

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            • Published in

              cover image ACM Other conferences
              EANN '15: Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS)
              September 2015
              266 pages
              ISBN:9781450335805
              DOI:10.1145/2797143

              Copyright © 2015 ACM

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              Publication History

              • Published: 25 September 2015

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              Acceptance Rates

              EANN '15 Paper Acceptance Rate36of60submissions,60%Overall Acceptance Rate36of60submissions,60%

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