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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, xxxx, x(x): x-xx 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, xxxx, x(x): x-xx 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 NNSFC 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
    120.5920.705-0.857-
    下载: 导出CSV

    Table  2.   Performance of SMR-LT indices forecast.

    p=1 hp=3 hp=6 hp=12 h
    ErmsCPeErmsCPeErmsCPeErmsCPe
    SMR-00Persistence8.9420.8380.67511.3840.7370.47413.4380.6330.26716.0490.477–0.046
    Model7.4680.8830.7738.6160.8360.69910.7930.7370.52713.3500.6080.276
    SMR-06Persistence5.9570.9020.8048.0120.8220.6459.8040.7340.46812.1720.5900.180
    Model5.7520.9250.8176.5920.8730.7597.6390.8250.6779.9760.7380.449
    SMR-12Persistence8.0540.8930.78611.8860.7670.53413.8980.6820.36316.8990.5290.059
    Model6.4730.9280.8629.8840.8260.67811.4850.7690.56513.8110.6650.371
    SMR-18Persistence9.0650.8950.79013.5170.7670.53416.3110.6610.32219.8510.498–0.005
    Model7.3160.9340.86410.6920.8440.70814.0220.7430.49916.8360.6130.277
    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-00Persistence0.8380.6750.7260.4520.5970.1940.352–0.296
    All-time0.8850.7830.8380.7000.7240.4850.5260.134
    Storm0.8870.7590.8400.7040.7340.4990.5520.257
    SMR-06Persistence0.8960.7930.8040.6080.6860.3720.457–0.087
    All-time0.9220.8230.8680.7480.8070.6420.6740.349
    Storm0.9240.8530.8710.7590.8180.6680.6910.455
    SMR-12Persistence0.8950.7890.7630.5250.6470.2930.406–0.189
    All-time0.9340.8720.8290.6740.7580.5320.5950.250
    Storm0.9350.8750.8400.7050.7700.5520.6180.316
    SMR-18Persistence0.8940.7880.7560.5130.6130.2260.361–0.278
    All-time0.9350.8720.8420.6980.7180.4350.5160.106
    Storm0.9360.8710.8510.6940.7360.4880.5590.214
    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 storms1–24.98±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 storms1–11.80±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
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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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