Neural Computing and Applications

, Volume 31, Issue 12, pp 8411–8422 | Cite as

Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting

  • Dinibel Pérez BelloEmail author
  • María P. Natali
  • Amalia Meza
Original Article


Accurate prediction of total electron content (TEC) is important for monitoring the behavior of the ionosphere and indeed a magnitude of interest to understand the properties and behavior of the Sun–Earth System. The conditions of this medium have a direct impact on a growing variety of critical technological infrastructure. This work presents a comparison between two different artificial neural networks (ANNs): an adaptive neuro-fuzzy inference system and nonlinear autoregressive neural network (NAR-NN) applied to TEC. Both ANNs where tested on four different geomagnetic locations on 4 1-week periods having a variety of geomagnetic disturbance levels. The effect of using different training period lengths and the system response for 60 and 30 min sampling rate TEC time series was investigated. NAR-NN shows a slightly better performance, being the higher difference during the greater perturbations. There is also a better response when sampling rates of 30 min are used.


vTEC Space weather Neural network Forecasting 



This research was supported by ANPCyT Grant PICT 2015-3710 and UNLP Grant 11/G142. The authors thank the International GNSS Service ( for providing the IONEX data and to the NASA/GSFC’s Space Physics Data Facility’s OMNIWeb Plus Service. Finally, we thank the two anonymous reviewers for their insightful comments on the original manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no conflict of interest regarding this manuscript.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Buenos AiresArgentina
  2. 2.Laboratorio de Meteorología espacial, Atmósfera terrestre, Geodesia, Geodinámica, diseño de Instrumental y Astrometría (MAGGIA)Universidad Nacional de La PlataLa PlataArgentina

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