Volume 36 Issue 6
Nov.  2016
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PENG Yuxiang, LÜ Jianyong, GU Saiju. Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm[J]. Chinese Journal of Space Science, 2016, 36(6): 866-874. doi: 10.11728/cjss2016.06.866
Citation: PENG Yuxiang, LÜ Jianyong, GU Saiju. Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm[J]. Chinese Journal of Space Science, 2016, 36(6): 866-874. doi: 10.11728/cjss2016.06.866

Application of Support Vector Machine to the Forecasting of Dst Index During Geomagnetic Storm

doi: 10.11728/cjss2016.06.866
  • Received Date: 2015-10-10
  • Rev Recd Date: 2016-04-01
  • Publish Date: 2016-11-15
  • In this study the support vector machine is applied to the forecasting of Dst index during intense geomagnetic storms (Dst≤ -100nT) that occurred from 1995 to 2014. We collect 2662 Dst indices and use the corresponding solar wind data as model input. We also build Neural Network and Linear machine as comparison, and improve the reliability of the predicted results by using K-fold cross validation. For comparison, we calculate the Correlation Coefficient (CC), the RMS errors, the Mean Absolute Error of the minimum Dst (Em) and the Mean Absolute Error of the time when the minimum Dst occurred (Et) between the observed Dst data and the predicted one. As a result, we find that SVM shows the best prediction performance for all events: CC is 0.89, RMS is 24.27nT, Em is 17.35nT and Et is 3.2 hours respectively. For further comparison, the 80 storm events are divided into two groups depending on the minimum value of Dst index. It is shown that the forecasting performance of SVM is better than other models both in the intense (-200 <Dstmin ≤-100nT) and the super intense geomagnetic storm (Dstmin ≤-200nT) groups.

     

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  • [1]
    BURTON R K, MCPHERRON R L, RUSSELL C T. An empirical relationship between interplanetary conditions and Dst[J]. J. Geophys. Res., 1975, 80:4204-4214
    [2]
    GONZALEZ W D, JOSELYN J A, KAMIDE Y, et al. What is a geomagnetic storm[J]. J. Geophys. Res., 1994, 99:5771
    [3]
    MURAYAMA T. Coupling function between the solar wind and the Dst index//Solar Wind-Magnetosphere Coupling[M]. Tokyo: Springer, 1986:119-126
    [4]
    THOMSEN M F, BOROVSKY J E, MCCOMAS D J, COLLIER M R. Variability of the ring current source population[J]. Geophys. Res. Lett., 1998, 25:3481
    [5]
    KLIMAS A J, VASSILIADIS D, BAKER D N. Dst index prediction using data-derived analogues of the magnetospheric dynamics[J]. J. Geophys. Res., 1998, 103:20435
    [6]
    BAN P P, SUN S J, CHEN C, ZHAO Z W. Forecasting of low-latitude storm-time ionospheric f0F2 using support vector machine[J]. Radio Sci., 2011, 46, RS6008. DOI: 10.1029/2010RS004633
    [7]
    WU J G, LUNDSTEDT H. Prediction of geomagnetic storms from solar wind data using Elman recurrent neural networks[J]. Geophys. Res. Lett., 1996, 23:319
    [8]
    LUNDSTEDT H, GLEISNER H, WINTOFT P. Operational forecasts of the geomagnetic Dst index[J]. Geophys. Res. Lett., 2002, 29(24):2181
    [9]
    JI E Y, MOON Y J, PARK J, et al. Comparison of neural network and support vector machine methods for Kp forecasting[J]. J. Geophys. Res., 2013, 118:5109-5117
    [10]
    LE G M, CAI Z, WANG H, ZHU Y. Solar cycle distribution of great geomagnetic storms[J]. Astrophys. Space Sci., 2012, 339:151-156
    [11]
    LE G M, CAI Z, WANG H, YIN Z, LI P. Solar cycle distribution of major geomagnetic storms[J]. Res. Astron. Astrophys., 2013, 13(6):739-748
    [12]
    VAPNIK V. Statistical Learning Theory[M]. New York: John Wiley, 1998
    [13]
    VAPNIK V. The Nature of Statistical Learning Theory[M]. New York: Springer-Verlag, 1995
    [14]
    GAVRISHCHAKA V V, GANGULI S B. Support vector machine as an efficient tool for high-dimensional data processing: application to substorm forecasting[J]. J. Geophys. Res., 2001, 106:29911-29914
    [15]
    LI R, CUI Y, HE H, WANG H. Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting[J]. Adv. Space Sci., 2008, 42:1469-1474
    [16]
    HUANG C, LIU D D, WANG J S. Forecast daily indices of solar activity, F10.7, using support vector regression method[J]. Res. Astron. Astrophys., 2009, 9:694-702
    [17]
    CHEN C, WU Z S, XU Z W, et al. Forecasting the local ionospheric f0F2 parameter 1 hour ahead during disturbed geomagnetic conditions[J]. J. Geophys. Res., 2010, 115, A11315. DOI: 10.1029/2010JA015529
    [18]
    LIU D D, HUANG C, LU J Y, WANG J S. The hourly average solar wind velocity prediction based on support vector regression method[J]. Mon. Not. Roy. Astron. Soc., 2011, 413:2877-2882
    [19]
    CHOI S, MOON Y J, VIEN N A, PARK Y D. Application of support vector machine to the prediction of geo-effective halo CMEs[J]. J. Korean Astron. Soc., 2012, 45:31-38
    [20]
    RODRÍGUEZ J D, PÉREZ A, LOZANO J A. Sensitivity analysis of k-fold cross validation in prediction error estimation[J]. IEEE Trans. Pattern Anal., 2010, 32(3):569-575
    [21]
    YANG F H, WHITE M A, MICHAELIS A R, et al. Prediction of continental-scale evapotranspiration by combining MODIS and AmeriFlux data through support vector machine[J]. IEEE Trans. Geosci. Remote Sens., 2006, 44(11):3452-3461
    [22]
    CRISTIANINI N, SHAWE-TAYLOR J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods[M]. Cambridge: Cambridge University Press, 2000
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