Twenty-four Hour Ahead Prediction of f₀F₂

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.