Volume 29 Issue 4
Jul.  2009
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Kong Qingyan, Liu Wen, Jiao Peinan, Fan Junmei, Feng Jing, Lu Zhuanxia. Twenty-four Hour Ahead Prediction of f0F2[J]. Journal of Space Science, 2009, 29(4): 377-382. doi: 10.11728/cjss2009.04.377
Citation: Kong Qingyan, Liu Wen, Jiao Peinan, Fan Junmei, Feng Jing, Lu Zhuanxia. Twenty-four Hour Ahead Prediction of f0F2[J]. Journal of Space Science, 2009, 29(4): 377-382. doi: 10.11728/cjss2009.04.377

Twenty-four Hour Ahead Prediction of f0F2

doi: 10.11728/cjss2009.04.377
  • Received Date: 1900-01-01
  • Rev Recd Date: 1900-01-01
  • Publish Date: 2009-07-15
  • Using three-layer feed-forward back propagation neural networks, twenty-four hour ahead prediction of the critical parameter of ionospheric F2 layer f0F2 is realized. The prediction model is developed based on 11 years of data (from 1976 to 1986) measured from China vertical station (Haikou, Guangzhou, Chongqing, Lhasa, Lanzhou, Beijing, Urumchi, Changchun, Manchuria). By analyzing time series correlation of f0F2 and solar-terrestrial activity, five input parameters are determined. The same-time training method is selected and the prediction values within 24 hour can be obtained without changing the network frame. By comparing the prediction property of Neural Network (NN) method and the autocorrelation one (named Corr), for quite data the NN method has higher accuracy except for summer data. While for the whole year data set, the Corr is better. In order to improve the applicability of the method for storm-time data, NN is corrected, and using two specified examples to explain the improvement in the article. After such modification, NN is better than Corr for the same test data as that used above.

     

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