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

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Abstract

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.

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Acknowledgements

This research was supported by ANPCyT Grant PICT 2015-3710 and UNLP Grant 11/G142. The authors thank the International GNSS Service (ftp://cddis.gsfc.nasa.gov) 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.

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  1. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Dinibel Pérez Bello, María P. Natali & Amalia Meza

  2. Laboratorio de Meteorología espacial, Atmósfera terrestre, Geodesia, Geodinámica, diseño de Instrumental y Astrometría (MAGGIA), Universidad Nacional de La Plata, La Plata, Argentina

    Dinibel Pérez Bello, María P. Natali & Amalia Meza

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  1. Dinibel Pérez Bello
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  2. María P. Natali
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Correspondence to Dinibel Pérez Bello.

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Pérez Bello, D., Natali, M.P. & Meza, A. Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting. Neural Comput & Applic 31, 8411–8422 (2019). https://doi.org/10.1007/s00521-019-04528-8

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