Forecasting the ionospheric foF2 in Chinese region by neural network technique

By using artificial neural network (NN) and considering the effects of the solar and geomagnetic activities on the ionosphere, a method for forecasting the ionospheric critical frequency, f_oF₂, up to 5 hour ahead at any target geographic location in Chinese region has been proposed. The inputs of the NN are time, day of the year, geographical latitude, solar zenith angle, the twelve recent past observations of f_oF₂ and the 30-day mean moving values of f_oF₂ from the target location. The outputs of the NN are F₊₁, F₊₂, F₊₃, F₊₄, F₊₅, representing the values of f_oF₂ up to 5h ahead. Data from Wulumqi, Changchun, Chongqing and Guangzhou stations spanning the period 1958-1968 are used for training the NN. Historical data at nine different stations in China are used to checkout the network respectively (Not including the training set). The performance of the NN is measured by calculating the root-mean-square error (RMS) difference between the NN outputs and measured station data. The results indicate that the prediction of NN has good agreement with measured data.