Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran

Abstract

In this paper, a new method of ionospheric tomography is developed and evaluated based on the neural networks (NN). This new method is named ITNN. In this method, wavelet neural network (WNN) with particle swarm optimization (PSO) training algorithm is used to solve some of the ionospheric tomography problems. The results of ITNN method are compared with the residual minimization training neural network (RMTNN) and modified RMTNN (MRMTNN). In all three methods, empirical orthogonal functions (EOFs) are used as a vertical objective function. To apply the methods for constructing a 3D-image of the electron density, GPS measurements of the Iranian permanent GPS network (in three days in 2007) are used. Besides, two GPS stations from international GNSS service (IGS) are used as test stations. The ionosonde data in Tehran (φ=35.73820, λ=51.38510) has been used for validating the reliability of the proposed methods. The minimum RMSE for RMTNN, MRMTNN, ITNN are 0.5312, 0.4743, 0.3465 (10¹¹ele./m³) and the minimum bias are 0.4682, 0.3890, and 0.3368 (10¹¹ele./m³) respectively. The results indicate the superiority of ITNN method over the other two methods.