On the Predictability of FoF2 using Support Vector Machine

Abstract

This paper proposes a method for forecasting the ionospheric critical frequency, foF2, one day in advance using the support vector machine approach. The output is the predicted foF2 one day ahead. The network is trained to use the ionospheric sounding data at Guangzhou station at high and low solar activity. The performance of the SVM model was verified with observed data. It is shown that the predicted foF2 has agreement with the observed foF2.