Evaluation of the effect of solar and geomagnetic parameters in spatio-temporal modeling of ionosphere's total electron content using machine learning methods

Document Type : Research Article


1 Department of Geomatics Engineering, Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran. E-mail: m.nezamzadeh97@gmail.com

2 Department of Geomatics Engineering, Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran. E-mail: vosoghi@kntu.ac.ir

3 Corresponding Author, Department of Geoscience Engineering, Faculty of Geoscience Engineering, Arak University of Technology, Arak, Iran. E-mail: mr.ghafari@arakut.ac.ir


The ionosphere is the upper part of the Earth's atmosphere, which is considered to be approximately 70 to 1000 km above the Earth's surface. Ionosphere modeling has been one of the goals of spatial geodesy since 1970. In many ionosphere modeling using satellite measurements such as GPS, total electron content (TEC) are used as observational input data. In recent years, modeling and prediction of the TEC have been considered by researchers with methods that have high speed and accuracy. One of the branches that has been able to show good capabilities in the field of estimation and modeling is machine learning methods (ML). Machine learning includes fuzzy inference systems (FIS), artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and evolutionary communications (ECs). Since 1993, with the advancement of computer technology, many new and hybrid algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), have been developed in ML. Another new effective approach in ML is the support vector regression (SVR) method. The SVR is a kernel-based ML method for classification and regression in which the risk of incorrect classification is minimized. The structure of an SVR network has a lot in common with the ANN, and the main difference is practically in the way of the training algorithm. In general, this method is divided into linear and nonlinear modes.
In this paper, the TEC of the ionosphere is modeled and evaluated with ML models. Support vector regression (SVR) and artificial neural network (ANN) methods are used for local TEC modeling. In both models, the latitude and longitude of the GPS stations, day of the year (DOY), hours, AP, KP, DST, and F10.7 are considered an input vectors. Also, the value of VTEC is considered as the output of the models. The main innovation of this paper is in evaluating the effect of different physical parameters on the accuracy of ML models. Using observations of 15 GPS stations in the northwest of Iran from 193 to 228 in 2012, new models are evaluated. Also, the results of the new models are compared with the results of the global ionosphere map (GIM), the IRI2016, and NeQuick empirical models in two internal and one external control station. Statistical indices of root mean square error (RMSE), relative error, dVTEC, and correlation coefficient are used to evaluate the error of the models. Sensitivity analysis of SVR and ANN models to input parameters is performed and the importance of each physical parameter in spatio-temporal modeling of the ionosphere is investigated. The results obtained from this paper show that in both high and low geomagnetic and solar activities, the SVR model in internal control stations has a higher accuracy than other models. But at the external control station, the error of the SVR model is much higher than other models. Determining the parameters of the kernel function using observations at the territory of the studied network is the reason. Also, the sensitivity of SVR and ANN models is increased to the physical parameters F10.7, KP, DST, and AP, respectively. For precise local ionosphere modeling, the effect of these parameters must also be considered.


Main Subjects

Amerian, Y., Voosoghi, B., & Mashhadi Hossainali, M. (2013). Regional Ionosphere Modeling in Support of IRI and Wavelet Using GPS Observations. Acta Geophysica, 61(5), 1246-1261, DOI: 10.2478/s11600-013-0121-5.
Bilitza, D., & Reinisch, B. W. (2008). International reference ionosphere 2007: Improvements and new parameters. Advances in space research, 42(4), 599-609.
Browne, S., Hargreaves, J., & Honary, B. (1995). An imaging riometer for ionospheric studies. Electronics & communication engineering journal, 7(5), 209-217.
Cander. R (1998), Artificial neural network applications in ionospheric studies. Annali di Geofisica, 5-6(1998), 757-766.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
Etemadfard, H., & Mashhadi Hossainali, M. (2016). Application of Slepian theory for improving the accuracy of SH‐based global ionosphere models in the Arctic region. Journal of Geophysical Research: Space Physics, 121(3), 2583-2594.
Feizi R, Voosoghi B, & Ghaffari Razin M. R. (2020). Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran. Advances in Space Research, 65, 2515–2528.
Ghaffari Razin, M. R., & Voosoghi, B. (2017). Ionosphere tomography using wavelet neural network and particle swarm optimization training algorithm in Iranian case study. GPS Solutions, 21(3), 1301-1314.
Ghaffari-Razin, M. R., & Voosoghi, B. (2018). Application of Wavelet Neural Networks for Improving of Ionospheric Tomography Reconstruction over Iran. Journal of the Earth and Space Physics, 44(4), 99-114.
Ghaffari Razin, M. R., & Voosoghi, B. (2016a). Modeling of ionosphere time series using wavelet neural networks (case study: NW of Iran). Advances in Space Research, 58(1), 74-83.
Ghaffari Razin, M. R., & Voosoghi, B. (2016b). Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content. Journal of Atmospheric and Solar-Terrestrial Physics, 149, 21-30.
Habarulema, J. B., McKinnell, L. A., & Opperman, B. D. (2011). Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks. Journal of Geophysical Research: Space Physics, 116(A4), 1-14.
Huang, Z., Li, Q., & Yuan, H. (2015). Forecasting of ionospheric vertical TEC 1-h ahead using a genetic algorithm and neural network. Adv. Space Res. 55, 1775–1783.
Haykin, S. (1994). Neural Networks: A Comprehensive Foundation, MacMillan College Publishing Co. New York.
Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2007). GNSS–global navigation satellite systems: GPS, GLONASS, Galileo, and more: Springer Science & Business Media.
Inyurt, S., & Sekertekin, A. (2019). Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN). Astrophysics and Space Science, 364(4), 1-8.
Jang, H., & Topal, E. (2014). A review of soft computing technology applications in several mining problems. Applied Soft Computing, 22, 638-651.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Komjathy, A. (1997). Global ionospheric total electron content mapping using the Global Positioning System. University of New Brunswick Fredericton.
Leick, A., Rapoport, L., & Tatarnikov, D. (2015). GPS satellite surveying: John Wiley & Sons.
Mars, P., Chen, J., Nambiar, R., & Fidler, J. (1996). Learning Algorithms: Theory and Applications in Signal Processing: CRC Press, Inc.
Muhtarov, P., Kutiev, I., & Cander, L., (2002). Geomagnetically correlated autoregression model for short-term prediction of ionospheric parameters. Inverse Problems. 18(1), 49.
Mautz, R., Ping, J., Heki, K., Schaffrin, B., Shum, C., & Potts, L. (2005). Efficient spatial and temporal representations of global ionosphere maps over Japan using B-spline wavelets. Journal of Geodesy, 78(11), 662-667.
Nava, B., Coisson, P., & Radicella, S. (2008). A new version of the NeQuick ionosphere electron density model. Journal of Atmospheric and Solar-Terrestrial Physics, 70(15), 1856-1862.
Nematipour, P., Raoofian-Naeeni, M., & Ghaffari Razin, M. R. (2021). Regional application of C1 finite element interpolation method in modeling of ionosphere total electron content over Europe. Advances in Space Research, 69(3), 1351-1365.
Sayin, I., Arikan, F., Arikan, O. (2008). Regional TEC mapping with random field priors and kriging. Radio Science, 43(5), 1-14.
Schunk, R.W., & Nagy, A.F. (2000). Ionospheres: Physics, Plasma Physics, and Chemistry, Cambridge University Press, 554.
Seeber, G. (2003). satellite geodesy: foundations. Methods and applications, Walter de Gruyter, Berlin and New York, 53.
Sharifi, M. A., & Farzaneh, S. (2015). Regional TEC dynamic modeling based on Slepian functions. Advances in Space Research, 56(5), 907-915.
Simpson, P. (1990). Artificial neural system-foundation, paradigm, application and implementation Pergamon Press New York.
Smola, A. J., & Schölkopf, B. (1998). On a kernel-based method for pattern recognition, regression, approximation, and operator inversion. Algorithmica, 22(1), 211-231.
Tebabal, A., Radicella, S., Damtie, B., Migoya-Orue, Y., Nigussie, M., & Nava, B. (2019). Feed forward neural network based ionospheric model for the East African region. Journal of Atmospheric and Solar-Terrestrial Physics, 191, 105052.
Vapnik, V. (1995). Support-vector Networks. Machine Learning, 20, 273-297.
Xia, G., Liu, Y., Wei, T., Wang, Z., Huang, W., Du, Z., Zhang, Z., Wang, X., & Zhou, C. (2021). Ionospheric TEC forecast model based on support vector machine with GPU acceleration in the China region. Advances in Space Research, 68(3), 1377-1389.
Yeganeh, B., Motlagh, M. S. P., Rashidi, Y., & Kamalan, H. (2012). Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmospheric Environment, 55, 357-365.