GPS and navigation GPS)، GLONASS، GALILEO
Tania Mansour Fallah; Behzad Voosoghi; Seyyed Reza Ghaffari-Razin
Volume 17, Issue 1 , March 2024, , Pages 21-36
Abstract
In this paper, the aim is to use the least squares support vector regression (LS-SVR) for spatio-temporal modeling of the ionospheric total electron content (TEC). In order to do this, the observations of 15 GPS stations in the north-west of Iran have been used in the period from 193 to 228 at 2012. ...
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In this paper, the aim is to use the least squares support vector regression (LS-SVR) for spatio-temporal modeling of the ionospheric total electron content (TEC). In order to do this, the observations of 15 GPS stations in the north-west of Iran have been used in the period from 193 to 228 at 2012. Comparing the results of the new model with support vector regression (SVR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), Kriging model, GIM and international reference ionosphere 2016 (IRI2016) as well as TEC obtained from GPS. The analyzes performed show that the averaged RMSE of ANN, ANFIS, SVR, LS-SVR, Kriging, GIM and IRI2016 models in two interior control stations are 3.91, 2.73, 1.27, 1.04, 2.70, 3.02 and 6.93 TECU, respectively. Also, the averaged relative error of the models in two interior control stations was calculated as 15.98%, 9.39%, 7.85%, 6.09%, 11.60%, 12.54% and 26.56%, respectively. Analysis of the PPP method shows an improvement of 50 mm in the coordinate components using the LS-SVR model. The results of this paper show that the LS-SVR model can be considered as an alternative to global and empirical models of the ionosphere in the study area.
GPS and navigation GPS)، GLONASS، GALILEO
Mir Reza Ghaffari Razin; Behzad Voosoghi
Volume 13, Issue 3 , September 2020, , Pages 39-50
Abstract
In this paper, WNN with PSO training algorithm is used to modeling and prediction of time-dependent ionosphere total electron content (TEC) variations. 2 different combinations of input observations are evaluated. The number of stations used to train of WNN with PSO algorithm selected 20 and 10. In all ...
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In this paper, WNN with PSO training algorithm is used to modeling and prediction of time-dependent ionosphere total electron content (TEC) variations. 2 different combinations of input observations are evaluated. The number of stations used to train of WNN with PSO algorithm selected 20 and 10. In all testing mode, 3 GPS stations with proper distribution are considered as a testing stations. Statistical indicators relative error, dVTEC and correlation coefficient were used to assess the wavelet neural network model. The results of proposed model compared with GPS-TEC and international reference ionosphere 2012 (IRI-2012) TEC. Average relative error computed in 3 test stations are 5.43% with 20 training station and 9.05% with 10 training station. Also the correlation coefficient calculated in 3 test stations are 0.954 with 20 training station and 0.907 with 10 training station. The results of this study show that the WNN with PSO algorithm is a reliable model to predict the temporal variations in the ionosphere.
M. R. Ghaffari; M. M. Hossainali
Volume 4, Issue 1 , July 2011, , Pages 1-14
Abstract
The function based approach to three-dimensional tomographic modeling of Ionosphere is analyzed. Harmonic and empirical orthogonal functions are used as the base functions required in the modeling of the horizontal and vertical variations of the electron density. The instability of solution has been ...
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The function based approach to three-dimensional tomographic modeling of Ionosphere is analyzed. Harmonic and empirical orthogonal functions are used as the base functions required in the modeling of the horizontal and vertical variations of the electron density. The instability of solution has been numerically analyzed and the Tikhonov regularization technique has been used to regularize the solution. To come up with an optimum value for the regularization parameter, the direct measurements of the electron density obtained from the Tehran Ionosonde station, located at λ=51.640 and φ=35.870 , are used. The electron density model reconstructed in this paper has a maximum relative error of 36.44% and its minimum value is 0.8503. The maximum difference between the vertical total electron content (VTEC) obtained from the reconstructed model to that obtained from the corresponding IGS network Ionosphere product is 52.320 TECU and its minimum value is 1.268.
