Efficiency of artificial neural networks in map of total electron content over Iran

Abstract

Maps of the total electron content (TEC) of the ionosphere can be reconstructed using data extracted from global positioning system (GPS) signals. For historic and other sparse data sets, the reconstruction of TEC images is often performed using multivariate interpolation techniques. In this paper, a quantitative comparison of the ability of artificial neural networks (ANN), polynomial fitting and kriging interpolation was carried out in order to model the spatial variations of TEC using GPS data over Iran. These methods are suitable for handling multi-scale phenomena and unevenly distributed data. The observations collected at 25 GPS stations from Iranian permanent GPS network (uniformly spread all over Iran with sampling rate of 30-seconds). Dual frequency carrier phase and code GPS observations were used. A smoothed TEC approach was used for absolute TEC recovery. Evaluation of the methods has been applied with single GPS station in Tehran equipped with ionosonde instrument. The minimum relative error for ANN, polynomial and kriging are 4.37, 6.35, 9.13 % and the maximum relative error are 8.61, 29.06, and 20.14 % respectively. Also root mean square error (RMSE) of 3.7 TECU is computed for ANN method which is less than RMSE of other mentioned methods. The results show that ANN method has higher accuracy and compiles speed than kriging and polynomial. As well as, it is found that polynomial and kriging methods required many computational points in adjustment step.