Diurnal specification of the ionospheric f0F2 parameter using a support vector machine

Abstract

This paper proposes a method for forecasting the ionospheric critical frequency, f0F2, up to 5 h ahead using the support vector machine (SVM) approach. The inputs to the SVM network are the universal time; day of the year; a 2 month running mean sunspot number (R2); a 3 day running mean of the 3 h planetary magnetic ap index, the solar zenith angle; the present value f0F2(t) and ten previously observed values f 0F2(t - i), where i = 1, 2, 3, 4, 19, 20, 21, 22, 23, 24; and the six derivatives of previous 30 day running means of f0F 2 fmF2(t - j), where j = 19, 20, 21, 22, 23, 24. The output is the predicted f0F2 up to 5 h ahead. The network is trained using the ionospheric sounding data from seven Chinese stations, i.e., Guangzhou, Haikou, Chongqing, Beijing, Lanzhou, Changchun, and Manzhouli stations at solar maximum and minimum. In order to test the predictive ability, the SVM was verified with different data from the training data. The quality of the proposed model prediction is evaluated by comparison with corresponding predictions from the persistence reference, the autocorrelation and the neural network (NN) models. By using data from seven Chinese stations, it is shown that the performance of the SVM model is superior to that of the autocorrelation and persistence models, and that it is comparable to that of the NN model. Copyright 2010 by the American Geophysical Union.