Skip to main content
Log in

A neural network approach for regional vertical total electron content modelling

  • Published:
Studia Geophysica et Geodaetica Aims and scope Submit manuscript


A Neural Network model has been developed for estimating the total electron content (TEC) of the ionosphere. TEC is proportional to the delay suffered by electromagnetic signals crossing the ionosphere and is among the errors that impact GNSS (Global Navigation Satellite Systems) observations. Ionospheric delay is particularly a problem for single frequency receivers, which cannot eliminate the (first-order) ionospheric delay by combining observations at two frequencies. Single frequency users rely on applying corrections based on prediction models or on regional models formed based on actual data collected by a network of receivers. A regional model based on a neural network has been designed and tested using data sets collected by the Brazilian GPS Network (RMBC) covering periods of low and high solar activity. Analysis of the results indicates that the model is capable of recovering, on average, 85% of TEC values.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  • Haykin S., 1999. Neural Networks-A Comprehensive Foundation. Prentice Hall, Upper Saddle River, New Jersey, USA.

    Google Scholar 

  • Hoffmann-Wellenhof B., Lichteneeger H. and Collins J., 2001. Global Positioning System: Theory and Practice. Springer-Verlag, Wien, New York.

    Google Scholar 

  • Klobuchar J.A., 1987. Ionospheric time-delay algorithm for single-frequency GPS users. IEEE Trans. Aerosp. Electron. Syst., 23, 325–331.

    Article  Google Scholar 

  • Komjathy A., 1997. Global Ionospheric Total Electron Content Mapping Using the Global Positioning System. Ph.D. Thesis, Department of Geodesy and Geomatics Engineering Technical Report No. 188, University of New Brunswick, Fredericton, New Brunswick, Canada.

    Google Scholar 

  • Leandro R.F. and Santos M.C., 2004. Comparison between autoregressive model and neural network for forecasting space environment parameters. Bollettino di Geodesia e Scienze Affini, LXII, N.3, 197–212.

    Google Scholar 

  • Leick A., 2004. GPS Satellite Surveying. John Wiley and Sons, Hoboken, New Jersey, USA.

    Google Scholar 

  • McKinnell L., 2002. A Neural Network Based Ionospheric Model for the Bottomside Electron Density Profile over Grahamstown, South Africa. Ph.D. Thesis, Rhodes University, Grahamstown, South Africa.

    Google Scholar 

  • Space Environment Center, 2004., accessed March 1st.

  • Tulunay Y., Tulunay E. and Senalp E.T., 2004a. The neural network technique-1: a general exposition. Adv. Space Res., 33, 983–987.

    Article  Google Scholar 

  • Tulunay Y., Tulunay E. and Senalp E.T., 2004b. The neural network technique-2: an ionospheric example illustrating its application. Adv. Space Res., 33, 988–992.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Reprints and permissions

About this article

Cite this article

Leandro, R.F., Santos, M.C. A neural network approach for regional vertical total electron content modelling. Stud Geophys Geod 51, 279–292 (2007).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI:

Key words