Deep neural network model for vertical total electron content prediction at a single low latitude station
Keywords:
Deep learning neural network, TEC model, Solar activity proxy, NeQuick 2Abstract
Modeling ionospheric parameters at low and equatorial stations is quite challenging due to the nature of the variation in the region. In this study, a Deep Neural Network (DNN) was configured via optimization of its hyperparameters and then trained to predict vertical Total Electron Content (vTEC) at a single low latitude location. Input parameters to the model are universal time, day of the year and solar activity index (EUV / ), while the target parameter is vTEC at a single location. EUV and values were used separately as the solar ionizing index leading to two trained models. The data used for training were for the solar cycle 24 and the data were split into 75 % for training and 25 % for validation. The training process was completed by the number of iterations. In addition, the derived model was also validated using data for the whole of 2000 and 2021 which are years outside the solar cycle 24. For completeness, the two models were also compared with NeQuick 2 model which is a global empirical model. The results obtained showed that the DNN models were able to predict reasonably well within the solar cycle 24 and slightly outperformed NeQuick 2 model. Similar results were obtained when the models were validated in 2021 with DNN slightly performed better that NeQuick 2 model. However, large deviation was recorded in 2000 – the DNN and NeQuick 2 models underestimated vTEC.
Published
How to Cite
Issue
Section
Copyright (c) 2025 F. U. Salifu, O. A. Oladipo, E. O. Ebock, B. Nava (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Similar Articles
- Muhammad Farman, Noreen Asghar, Muhammad Umer Saleem, Nezihal Gokbulut, Evren Hincal, Aseel Smerat, Mohamed Hafez, Optimal control of smoking-induced asthma and cardiovascular disorders with medical and public health interventions
- Oluwayemisi Oyeronke Alaba, B. M. Golam Kibria, The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application
- Idris Babaji Muhammad, Salisu Usaini, Dynamics of Toxoplasmosis Disease in Cats population with vaccination , Journal of the Nigerian Society of Physical Sciences: Volume 3, Issue 1, February 2021
- Sunday D. Olorunfunmi, Armand Bahini, Samuel A. Adeojo, Velocity-in/dependent double folding analysis of 12C + 12C elastic scattering cross section at different energies
- A. E. Ibor, E. B. Edim, A. A. Ojugo, Secure Health Information System with Blockchain Technology
- Tunde Tajudeen Yusuf, Afeez Abidemi, Ayodeji Sunday Afolabi, Emmanuel Jesuyon Dansu, Optimal Control of the Coronavirus Pandemic with Impacts of Implemented Control Measures
- A. E. Ajetunmobi, A. O. Musthapha, I. C. Okeyode, A. M. Gbadebo, D. Al-Azmi, T. W. David, Assessing the need for radiation protection measures in artisanal and small scale mining of tantalite in Oke-Ogun, Oyo State, Nigeria
- Opeyemi Odetunde, Tunde Alesinloye, Omoniyi Francis, Modeling the dynamics of type 2 diabetes with modifiable lifestyle influence
- S. O. Salawu, R. A. Kareem, J. O. Ajilore, Eyring-Powell MHD nanoliquid and entropy generation in a porous device with thermal radiation and convective cooling
- Wilson Nwankwo, Kingsley Ukhurebor, Investigating the Performance of Point to Multipoint Microwave Connectivity across Undulating Landscape during Rainfall
You may also start an advanced similarity search for this article.

