Diurnal specification of the ionospheric f(0)F(2) parameter using a support vector machine

作者：Chen, Chun^*; Wu, Zhen-Sen; Ban, Pan-Pan; Sun, Shu-Ji; Xu, Zheng-Wen; Zhao, Zhen-Wei

来源：Radio Science, 2010, 45: RS5018.

DOI：10.1029/2010RS004393

• NEURAL-NETWORKS
• SUNSPOT NUMBER
• SOLAR-FLARE
• PREDICTION
• F(O)F(2)
• MODEL
• INTERPOLATION

This paper proposes a method for forecasting the ionospheric critical frequency, f(0)F(2), 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 f(0)F(2)(t) and ten previously observed values f(0)F(2)(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 f(0)F(2) f(m)F(2)(t - j), where j = 19, 20, 21, 22, 23, 24. The output is the predicted f(0)F(2) 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.

• 出版日期2010-10-29
• 单位西安电子科技大学