Efficiency of the least squares support vector regression in local modeling of the ionosphere total electron content and comparison with other models

Mansour Fallah, Tania; Voosoghi, Behzad; Ghaffari-Razin, Seyyed Reza

doi:10.22034/jsst.2024.1454

Document Type : Research Paper

Authors

¹ Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

² Department of Surveying Engineering, Arak University of Technology, Arak, Iran

https://doi.org/10.22034/jsst.2024.1454

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. 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.

Keywords

Main Subjects

GPS and navigation GPS)، GLONASS، GALILEO

References

[1] I. Sayin, F. Arikan, and O. Arikan, "Regional TEC mapping with random field priors and kriging," Radio Science, vol. 43, no. 05, pp. 1-14, 2008, doi: https://doi.org/10.1029/2007RS003786 .

[2] M.-R. G. Razin, "Development and analysis of 3D ionosphere modeling using base functions and GPS data over Iran," Acta Geodaetica et Geophysica, vol. 51, pp. 95-111, 2016,

[3] M. R. G. Razin and B. Voosoghi, "Regional ionosphere modeling using spherical cap harmonics and empirical orthogonal functions over Iran," Acta Geodaetica et Geophysica, vol. 52, pp. 19-33, 2017, doi: https://doi.org/10.1007/s40328-016-0162-8 .

[4] Amerian, Y., Voosoghi, B., and Y. Amerian, B. Voosoghi, and M. M. Hossainali, "Regional ionosphere modeling in support of IRI and wavelet using GPS observations," Acta Geophysica, vol. 61, pp. 1246-1261, 2013.

[5] H. Etemadfard and M. Mashhadi Hossainali, "Application of Slepian theory for improving the accuracy of SH‐based global ionosphere models in the Arctic region," Journal of Geophysical Research: Space Physics, vol. 121, no. 3, pp. 2583-2594, 2016, doi: https://doi.org/10.1002/2015JA021811 .

[6] M. A. Sharifi and S. Farzaneh, "Regional TEC dynamic modeling based on Slepian functions," Advances in Space Research, vol. 56, no. 5, pp. 907-915, 2015, doi: https://doi.org/10.1016/j.asr.2015.05.024 .

[7] R. Mautz, J. Ping, K. Heki, B. Schaffrin, C. Shum, and L. Potts, "Efficient spatial and temporal representations of global ionosphere maps over Japan using B-spline wavelets," Journal of Geodesy, vol. 78, pp. 662-667, 2005, doi: https://doi.org/10.1007/s00190-004-0432-z .

[8] P. Muhtarov, I. Kutiev, and L. Cander, "Geomagnetically correlated autoregression model for short-term prediction of ionospheric parameters," Inverse Problems, vol. 18, no. 1, p. 49, 2002, doi: 10.1088/0266-5611/18/1/304.

[9] P. Nematipour, M. Raoofian-Naeeni, and M. R. G. Razin, "Regional application of C1 finite element interpolation method in modeling of ionosphere total electron content over Europe," Advances in space research, vol. 69, no. 3, pp. 1351-1365, 2022, doi: https://doi.org/10.1016/j.asr.2021.11.030.

[10] H. Jang and E. Topal, "A review of soft computing technology applications in several mining problems," Applied Soft Computing, vol. 22, pp. 638-651, 2014, doi: https://doi.org/10.1016/j.asoc.2014.05.019.

[11] L. R. Cander, R. Leitinger, and M. Levy, "Ionospheric models including the auroral environment," in Workshop on Space Weather, Report WPP-155, Noordwijk, The Netherlands, European Space Agency (ISSN 1022-6656), 1999, pp. 135-142.

[12] J. B. Habarulema, L. A. McKinnell, and B. D. Opperman, "Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks," Journal of geophysical research: space physics, vol. 116, no. A4, 2011.

[13] M. R. G. Razin, B. Voosoghi, and A. Mohammadzadeh, "Efficiency of artificial neural networks in map of total electron content over Iran," Acta Geodaetica et Geophysica, vol. 51, pp. 541-555, 2016, doi: 10.1007/s40328-015-0143-3.

[14] A. Tebabal, S. Radicella, B. Damtie, Y. Migoya-Orue, M. Nigussie, and B. Nava, "Feed forward neural network based ionospheric model for the East African region," Journal of atmospheric and solar-terrestrial physics, vol. 191, p. 105052, 2019, doi: https://doi.org/10.1016/j.jastp.2019.05.016 .

[15] S. Inyurt and A. Sekertekin, "Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN)," Astrophysics and Space Science, vol. 364, no. 4, p. 62, 2019, doi: https://doi.org/10.1007/s10509-019-3545-9 .

[16] R. Feizi, B. Voosoghi, and M. R. G. Razin, "Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran," Advances in Space Research, vol. 65, no. 11, pp. 2515-2528, 2020, doi: https://doi.org/10.1016/j.asr.2020.02.027 .

[17] C. Cesaroni, L. Spogli, A. Aragon-Angel, M. Fiocca, V. Dear, G. De Franceschi, and V. Romano, "Neural network based model for global Total Electron Content forecasting," Journal of space weather and space climate, vol. 10, p. 11, 2020, , doi: https://doi.org/10.1051/swsc/2020013 .

[18] L. Liu, S. Zou, Y. Yao, and Z. Wang, “Forecasting global ionospheric TEC using deep learning approach,” Space Weather, vol. 18, no. 11, pp. e2020SW002501, 2020, doi: https://doi.org/10.1029/2020SW002501.

[19] G. Xia, Y. Liu, T. Wei, Z. Wang, W. Huang, Z. Du, Z. Zhang, X. Wang, and C. Zhou, “Ionospheric TEC forecast model based on support vector machine with GPU acceleration in the China region,” Advances in Space Research, vol. 68, no. 3, pp. 1377-1389, 2021, doi: https://doi.org/10.1016/j.asr.2021.03.021.

[20] M. Adolfs, and M. M. Hoque, “A neural network-based TEC model capable of reproducing nighttime winter anomaly,” Remote Sensing, vol. 13, no. 22, pp. 4559, 2021, doi: https://doi.org/10.3390/rs13224559.

[21] A. J. Smola, and B. Schölkopf, “On a kernel-based method for pattern recognition, regression, approximation, and operator inversion,” Algorithmica, vol. 22, pp. 211-231, 1998, doi: https://doi.org/10.1007/PL00013831 .

[22] M.-R. Ghaffari Razin and S. Inyurt, "Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde," GPS Solutions, vol. 26, pp. 1-13, 2022, doi: https://doi.org/10.1007/s10291-021-01184-1.

[23] D. Bilitza, L.-A. McKinnell, B. Reinisch, and T. Fuller-Rowell, "The international reference ionosphere today and in the future," Journal of Geodesy, vol. 85, pp. 909-920, 2011.

[24] L. Ciraolo, F. Azpilicueta, C. Brunini, A. Meza, and S. M. Radicella, "Calibration errors on experimental slant total electron content (TEC) determined with GPS," Journal of geodesy, vol. 81, pp. 111-120, 2007, doi: https://doi.org/10.1007/s00190-006-0093-1 .

[25] C. Cortes and V. Vapnik, , "Support-vector networks," Machine learning, vol. 20, , pp. 273-297, 1995, doi: https://doi.org/10.1007/BF00994018 .

[26] J. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, "Least squaress Support Vector Machines". https://doi.org/10.1142/5089.

[27] G. Matheron, "The theory of regionalized variables and its applications: Centre de Geostatistique," Fontainebleau, France, 1971.

[28] V. R. Joseph, “Limit kriging,” Technometrics, vol. 48, no. 4, pp. 458-466, 2006, doi: https://doi.org/10.1198/004017006000000011 .

[29] S. Erdoğan, “Modelling the spatial distribution of DEM error with geographically weighted regression: An experimental study,” Computers & Geosciences, vol. 36, no. 1, pp. 34-43, 2010, doi: https://doi.org/10.1016/j.cageo.2009.06.005 .

[30] J. Li and A. D. Heap, A Review of Spatial Interpolation Methods for Environmental Scientists. Geoscience Australia Canberra, 2008.

[31] S. Schaer, and S. h. d. s. n. C. géodésique, Mapping and predicting the Earth's ionosphere using the Global Positioning System: Institut für Geodäsie und Photogrammetrie, Eidg. Technische Hochschule …, 1999.

[32] M. S. Nezamzadeh, B. Voosoghi, S. R. Ghaffari Razin, "Evaluation of the effect of solar and geomagnetic parameters in spatio-temporal modeling of ionosphere's total electron content using machine learning methods," journal of the earth and space physisc. Vol. 49, no. 1, pp. 153-169.

Space Science and Technology

Efficiency of the least squares support vector regression in local modeling of the ionosphere total electron content and comparison with other models

References

References

Volume 17, Issue 1 - Serial Number 60
March 2024
Pages 21-36

Efficiency of the least squares support vector regression in local modeling of the ionosphere total electron content and comparison with other models

References

References

Volume 17, Issue 1 - Serial Number 60March 2024Pages 21-36

Volume 17, Issue 1 - Serial Number 60
March 2024
Pages 21-36