Abstract
Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.
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References
Akhoondzadeh M (2013) Support vector machines for TEC seismo-ionospheric anomalies detection. Annales Geophysicae. Copernicus GmbH pp. 173–86
Ban P-P, Sun S-J, Chen C, Zhao Z-W (2011) Forecasting of low-latitude storm-time ionospheric foF2 using support vector machine. Radio Sci 46:1–9
Bilitza D, Altadill D, Truhlik V et al (2017) International Reference Ionosphere 2016: from ionospheric climate to real-time weather predictions. Space Weather 15:418–429
Cesaroni C, Spogli L, Aragon-Angel A. et al. (2020) Neural network based model for global total electron content forecasting. J Space Weather Space Clim
Chen C, Wu Z-S, Ban P-P, Sun S-J, Xu Z-W, Zhao Z-W (2010) Diurnal specification of the ionospheric f 0 F 2 parameter using a support vector machine. Radio Sci 45:1–13
European GNSS (Galileo) Open Service (2016) Ionospheric correction algorithm for Galileo single frequency users. https://www.gsc-europa.eu/system/files/galileo_documents/Galileo_Ionospheric_Model.pdf
Harsha PBS, Ratnam DV, Nagasri ML, Sridhar M, Raju KP (2020) Kriging-based ionospheric TEC, ROTI and amplitude scintillation index (S 4) maps for India. IET Radar Sonar Navig 14:1827–1836
Hofmann-Wellenhof B, Lichtenegger H, Collins J (2012) Global positioning system: theory and practice. Springer Science & Business Media
Hu J, Wang J, Zeng G (2013) A hybrid forecasting approach applied to wind speed time series. Renew Energy 60:185–194
Kim M, Kim J (2019) Extending the coverage area of regional ionosphere maps using a support vector machine algorithm. Ann Geophys 37(1):77–87
Mallika L, Ratnam DV, Raman S, Sivavaraprasad G (2020) Performance analysis of neural networks with IRI-2016 and IRI-2012 models over Indian low-latitude GPS stations. Astrophys Space Sci 365:1–14
Meyer D, Wien FT (2015) Support vector machines. The Interface to libsvm in package e1071 28
Mukesh R, Soma P, Karthikeyan V, Sindhu P (2019) Prediction of ionospheric vertical total electron content from GPS data using ordinary kriging-based surrogate model. Astrophys Space Sci 364:15
Okoh D (2018) GPS modeling of the ionosphere using computer neural networks. Multifunctional Operation and Application of GPS. IntechOpen
Parkinson BW, Enge P, Axelrad P, Spilker JJ Jr (1996) Global positioning system: Theory and applications, vol II. American Institute of Aeronautics and Astronautics
Razin MRG, Voosoghi B (2016) Modeling of ionosphere time series using wavelet neural networks (case study: N.W. of Iran). Adv Space Resh 58:74–83
Razin MRG, Voosoghi B, Mohammadzadeh A (2016) Efficiency of artificial neural networks in map of total electron content over Iran. Acta Geod Geoph 51:541–555
Seemala GK, Valladares CE (2011) Statistics of total electron content depletions observed over the South American Continent for the year 2008. Radio Sci 46, RS5019
Stoean R, Dumitrescu D, Preuss M, Stoean C (2006) Evolutionary support vector regression machines. In: 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, pp. 330–5
Vladimir N.Vapnik (1995) The nature of statistical learning theory
Watthanasangmechai K, Supnithi P, Lerkvaranyu S, Tsugawa T, Nagatsuma T, Maruyama T (2012) TEC prediction with neural network for equatorial latitude station in Thailand. Earth, Planet Space 64:473–483
Zhukov A, Sidorov D, Mylnikova A, Yasyukevich Y (2018) Machine learning methodology for ionosphere total electron content nowcasting. Int J Artif Intell 16:144–157
Acknowledgements
The present work has been carried out under the research project titled "Implementation of Deep Learning Algorithms to Develop Web based Ionospheric Time Delays Forecasting System over Indian Region using Ground based GNSS and NAVigation with Indian Constellation (NAVIC) observations" sponsored by Science& Engineering Research Board (SERB) (A statutory body of the Department of Science & Technology, Government of India) New Delhi, India, vide sanction order No: ECR/2018/001701, and Department of Science and Technology, New Delhi, India for funding this research through SR/FST/ET-II/2019/450 FIST program. Thanks for the Scripps Orbit and Permanent Array Centre (SOPAC) to provide the GNSS data at http://sopac.ucsd.edu for IGS stations and OMNIWEB Data Explorer-NASA (omniweb.gsfc.nasa.gov/form/dx1.html) for making the availability of soar and geomagnetic data online.
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KS, GS contributed to data processing; DVR contributed to validation; KS, DVR contributed to original draft preparation; DVR contributed to review; KS, DVR contributed to final editing.
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Sivakrishna, K., Ratnam, D.V. & Sivavaraprasad, G. Support Vector Regression model to predict TEC for GNSS signals. Acta Geophys. 70, 2827–2836 (2022). https://doi.org/10.1007/s11600-022-00954-w
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DOI: https://doi.org/10.1007/s11600-022-00954-w