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Prediction of Partial Ring Current Index Using LSTM Neural Network

LI Hui WANG Runze WANG Chi

LI Hui, WANG Runze, WANG Chi. Prediction of Partial Ring Current Index Using LSTM Neural Network. Chinese Journal of Space Science, 2022, 42(5): 873-883 doi: 10.11728/cjss2022.05.210513061
Citation: LI Hui, WANG Runze, WANG Chi. Prediction of Partial Ring Current Index Using LSTM Neural Network. Chinese Journal of Space Science, 2022, 42(5): 873-883 doi: 10.11728/cjss2022.05.210513061

Prediction of Partial Ring Current Index Using LSTM Neural Network

doi: 10.11728/cjss2022.05.210513061
Funds: Supported by National Natural Science Foundation of China grants (42022032, 41874203, 42188101), project of Civil Aerospace “13 th Five Year Plan” Preliminary Research in Space Science (D020301, D030202), Strategic Priority Research Program of CAS (XDA17010301), Key Research Program of Frontier Sciences CAS (QYZDJ-SSW-JSC028), and International Partner-National Program of CAS (183311 KYSB20200017)
More Information
  • Figure  1.  Partial ring current indices during the magnetic storm on 26 August 2018

    Figure  2.  Statistical characteristics of SMR-LT minimum and its time lag to SMR minimum for 318 magnetic storms

    Figure  3.  Performance of models in forecasting the SMR index using different sequence length ($ s $). The four panels represent the results when different $ p $ are considered

    Figure  4.  Scatter plot of the model’s predictions of SMR-LT indices on the test set. The blue line represents the fitting result, and the black dotted line represents the exact prediction

    Figure  5.  Evolution of observed (in black) and predicted (in blue) SMR-LT indices during the storm on 26 August 2018

    Table  1.   Comparison between Ref. [14], Ref. [22], Ref. [23], and our proposed model

    p / hPersistenceOur modelRef. [14]Ref. [22]Ref. [23]
    10.9450.9650.9660.8450.978
    30.8530.9030.9230.8720.895
    60.7550.8240.8650.8640.788
    120.5920.7050.857
    下载: 导出CSV

    Table  2.   Performance of SMR-LT indices forecast

    p=1 hp=3 hp=6 hp=12 h
    ErmsCPeErmsCPeErmsCPeErmsCPe
    SMR-00 Persistence 8.942 0.838 0.675 11.384 0.737 0.474 13.438 0.633 0.267 16.049 0.477 –0.046
    Model 7.468 0.883 0.773 8.616 0.836 0.699 10.793 0.737 0.527 13.350 0.608 0.276
    SMR-06 Persistence 5.957 0.902 0.804 8.012 0.822 0.645 9.804 0.734 0.468 12.172 0.590 0.180
    Model 5.752 0.925 0.817 6.592 0.873 0.759 7.639 0.825 0.677 9.976 0.738 0.449
    SMR-12 Persistence 8.054 0.893 0.786 11.886 0.767 0.534 13.898 0.682 0.363 16.899 0.529 0.059
    Model 6.473 0.928 0.862 9.884 0.826 0.678 11.485 0.769 0.565 13.811 0.665 0.371
    SMR-18 Persistence 9.065 0.895 0.790 13.517 0.767 0.534 16.311 0.661 0.322 19.851 0.498 –0.005
    Model 7.316 0.934 0.864 10.692 0.844 0.708 14.022 0.743 0.499 16.836 0.613 0.277
    下载: 导出CSV

    Table  3.   Performance of storm-time SMR-LT indices prediction

    p=1 hp=3 hp=6 hp=12 h
    CPeCPeCPeCPe
    SMR-00 Persistence 0.838 0.675 0.726 0.452 0.597 0.194 0.352 –0.296
    All-time 0.885 0.783 0.838 0.700 0.724 0.485 0.526 0.134
    Storm 0.887 0.759 0.840 0.704 0.734 0.499 0.552 0.257
    SMR-06 Persistence 0.896 0.793 0.804 0.608 0.686 0.372 0.457 –0.087
    All-time 0.922 0.823 0.868 0.748 0.807 0.642 0.674 0.349
    Storm 0.924 0.853 0.871 0.759 0.818 0.668 0.691 0.455
    SMR-12 Persistence 0.895 0.789 0.763 0.525 0.647 0.293 0.406 –0.189
    All-time 0.934 0.872 0.829 0.674 0.758 0.532 0.595 0.250
    Storm 0.935 0.875 0.840 0.705 0.770 0.552 0.618 0.316
    SMR-18 Persistence 0.894 0.788 0.756 0.513 0.613 0.226 0.361 –0.278
    All-time 0.935 0.872 0.842 0.698 0.718 0.435 0.516 0.106
    Storm 0.936 0.871 0.851 0.694 0.736 0.488 0.559 0.214
    下载: 导出CSV

    Table  4.   Magnetic storm intensity distribution

    Moderate stormsIntense stormsTotal
    Training set11972191
    Validation set531366
    Test set52961
    Total22494318
    下载: 导出CSV

    Table  5.   Relative errors (mean±standard deviation) of storm intensity predictions

    p / hRelative error / (%)
    SMR-00SMR-06SMR-12SMR-18
    52 moderate storms 1 –24.98±7.18 –10.99±7.73 –9.81±13.89 –12.49±12.10
    3 –23.86±11.93 –13.73±13.84 –19.79±16.57 –24.42±14.52
    6 –37.34±13.34 –16.88±14.66 –28.40±15.93 –32.85±13.80
    9 intense storms 1 –11.80±9.16 –9.93±3.28 –8.76±9.73 –10.56±10.92
    3 –13.04±13.28 –13.67±6.16 –23.19±14.57 –23.19±13.19
    6 –31.10±13.58 –19.70±10.10 –34.66±12.02 –33.56±12.53
    下载: 导出CSV
  • [1] DUNGEY J W. Interplanetary magnetic field and the Auroral zones[J]. Physical Review Letters, 1961, 6(2): 47-48 doi: 10.1103/PhysRevLett.6.47
    [2] GONZALEZ W D, JOSELYN J A, KAMIDE Y, et al. What is a geomagnetic storm[J]. Journal of Geophysical Research: Space Physics, 1994, 99(A4): 5771-5792 doi: 10.1029/93JA02867
    [3] BURTON R K, MCPHERRON R L, RUSSELL C T. An empirical relationship between interplanetary conditions and Dst[J]. Journal of Geophysical Research, 1975, 80(31): 4204-4214 doi: 10.1029/JA080i031p04204
    [4] DAGLIS I A, THORNE R M, BAUMJOHANN W, et al. The terrestrial ring current: origin, formation, and decay[J]. Reviews of Geophysics, 1999, 37(4): 407-438 doi: 10.1029/1999RG900009
    [5] KAMIDE Y, BAUMJOHANN W, DAGLIS I A, et al. Current understanding of magnetic storms: storm-substorm relationships[J]. Journal of Geophysical Research: Space Physics, 1998, 103(A8): 17705-17728 doi: 10.1029/98JA01426
    [6] WANG C B, CHAO J K, LIN C H. Influence of the solar wind dynamic pressure on the decay and injection of the ring current[J]. Journal of Geophysical Research: Space Physics, 2003, 108(A9): 1341 doi: 10.1029/2003JA009851
    [7] FENRICH F R, LUHMANN J G. Geomagnetic response to magnetic clouds of different polarity[J]. Geophysical Research Letters, 1998, 25(15): 2999-3002 doi: 10.1029/98GL51180
    [8] O’BRIEN T P, MCPHERRON R L. Forecasting the ring current index Dst in real time[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2000, 62(14): 1295-1299 doi: 10.1016/S1364-6826(00)00072-9
    [9] LUNDSTEDT H, WINTOFT P. Prediction of geomagnetic storms from solar wind data with the use of a neural network[J]. Annales Geophysicae, 1994, 12(1): 19-24 doi: 10.1007/s00585-994-0019-2
    [10] LUNDSTEDT H, GLEISNER H, WINTOFT P. Operational forecasts of the geomagnetic Dst index[J]. Geophysical Research Letters, 2002, 29(24): 2181 doi: 10.1029/2002GL016151
    [11] WU J G, LUNDSTEDT H. Prediction of geomagnetic storms from solar wind data using Elman Recurrent Neural Networks[J]. Geophysical Research Letters, 1996, 23(4): 319-322 doi: 10.1029/96GL00259
    [12] WEI H L, ZHU D Q, BILLINGS S A, et al. Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks[J]. Advances in Space Research, 2007, 40(12): 1863-1870 doi: 10.1016/j.asr.2007.02.080
    [13] LAZZÚS J A, LÓPEZ-CARABALLO C H, ROJAS P, et al. Forecasting of DST index from auroral electrojet indices using time-delay neural network + particle swarm optimization[J]. Journal of Physics:Conference Series, 2016, 720: 012001 doi: 10.1088/1742-6596/720/1/012001
    [14] GRUET M A, CHANDORKAR M, SICARD A, et al. Multiple-hour-ahead forecast of the Dst index using a combination of long short-term memory neural network and Gaussian process[J]. Space Weather, 2018, 16(11): 1882-1896 doi: 10.1029/2018SW001898
    [15] BOYNTON R J, BALIKHIN M A, BILLINGS S A, et al. Data derived NARMAX Dst model[J]. Annales Geophysicae, 2011, 29(6): 965-971 doi: 10.5194/angeo-29-965-2011
    [16] LI H, WANG C, TU C, et al. Machine learning approach for solar wind categorization[J]. Earth and Space Science, 2020, 7(5): e2019EA000997 doi: 10.1029/2019EA000997
    [17] LU J Y, PENG Y X, WANG M, et al. Support Vector Machine combined with distance correlation learning for Dst forecasting during intense geomagnetic storms[J]. Planetary and Space Science, 2016, 120: 48-55 doi: 10.1016/j.pss.2015.11.004
    [18] LI H, WANG C, KAN J R. Contribution of the partial ring current to the SYMH index during magnetic storms[J]. Journal of Geophysical Research: Space Physics, 2011, 116(A11): A11222 doi: 10.1029/2011JA016886
    [19] NEWELL P T, GJERLOEV J W. SuperMAG-based partial ring current indices[J]. Journal of Geophysical Research: Space Physics, 2012, 117(A5): A05215 doi: 10.1029/2012JA017586
    [20] BENGIO Y, SIMARD P, FRASCONI P. Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166 doi: 10.1109/72.279181
    [21] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780 doi: 10.1162/neco.1997.9.8.1735
    [22] LETHY A, EL-ERAKI M A, SAMY A, et al. Prediction of the Dst index and analysis of its dependence on solar wind parameters using neural network[J]. Space Weather, 2018, 16(9): 1277-1290 doi: 10.1029/2018SW001863
    [23] LAZZÚS J A, VEGA P, ROJAS P, et al. Forecasting the Dst index using a swarm-optimized neural network[J]. Space Weather, 2017, 15(8): 1068-1089 doi: 10.1002/2017SW001608
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出版历程
  • 收稿日期:  2021-05-13
  • 录用日期:  2021-05-28
  • 修回日期:  2022-02-17
  • 网络出版日期:  2022-09-20

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