A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data

Sani Abubakar; Daniel Okoh; Bello Idrith Tijjani; Rabia Salihu Said; Benjamin Ayantunji Gbenro; Enoch O. Elemo

doi:doi:10.11648/j.ijass.20241203.11

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A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data

Sani Abubakar^*

, Daniel Okoh, Bello Idrith Tijjani, Rabia Salihu Said, Benjamin Ayantunji Gbenro, Enoch O. Elemo

Published in International Journal of Astrophysics and Space Science (Volume 12, Issue 3)

Received: 9 November 2024 Accepted: 17 December 2024 Published: 30 December 2024

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Abstract

This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in ionPrf file format provided by the COSMIC team. The profiles are in netCDF format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00^oN, 8.59^oE; geomagnetic coordinates: 0.47^oN). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 10³ electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).

Published in	International Journal of Astrophysics and Space Science (Volume 12, Issue 3)
DOI	10.11648/j.ijass.20241203.11
Page(s)	63-74
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

COSMIC Mission, Electron Density, Ionosphere, Neural Network, Radio Occultation, Ionosonde

References

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Abubakar, S., Okoh, D., Tijjani, B. I., Said, R. S., Gbenro, B. A., et al. (2024). A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. International Journal of Astrophysics and Space Science, 12(3), 63-74. https://doi.org/10.11648/j.ijass.20241203.11

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ACS Style

Abubakar, S.; Okoh, D.; Tijjani, B. I.; Said, R. S.; Gbenro, B. A., et al. A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. Int. J. Astrophys. Space Sci. 2024, 12(3), 63-74. doi: 10.11648/j.ijass.20241203.11

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AMA Style

Abubakar S, Okoh D, Tijjani BI, Said RS, Gbenro BA, et al. A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. Int J Astrophys Space Sci. 2024;12(3):63-74. doi: 10.11648/j.ijass.20241203.11

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@article{10.11648/j.ijass.20241203.11,
  author = {Sani Abubakar and Daniel Okoh and Bello Idrith Tijjani and Rabia Salihu Said and Benjamin Ayantunji Gbenro and Enoch O. Elemo},
  title = {A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data
},
  journal = {International Journal of Astrophysics and Space Science},
  volume = {12},
  number = {3},
  pages = {63-74},
  doi = {10.11648/j.ijass.20241203.11},
  url = {https://doi.org/10.11648/j.ijass.20241203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijass.20241203.11},
  abstract = {This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in ionPrf file format provided by the COSMIC team. The profiles are in netCDF format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00oN, 8.59oE; geomagnetic coordinates: 0.47oN). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 103 electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).
},
 year = {2024}
}

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TY  - JOUR
T1  - A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data

AU  - Sani Abubakar
AU  - Daniel Okoh
AU  - Bello Idrith Tijjani
AU  - Rabia Salihu Said
AU  - Benjamin Ayantunji Gbenro
AU  - Enoch O. Elemo
Y1  - 2024/12/30
PY  - 2024
N1  - https://doi.org/10.11648/j.ijass.20241203.11
DO  - 10.11648/j.ijass.20241203.11
T2  - International Journal of Astrophysics and Space Science
JF  - International Journal of Astrophysics and Space Science
JO  - International Journal of Astrophysics and Space Science
SP  - 63
EP  - 74
PB  - Science Publishing Group
SN  - 2376-7022
UR  - https://doi.org/10.11648/j.ijass.20241203.11
AB  - This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in ionPrf file format provided by the COSMIC team. The profiles are in netCDF format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00oN, 8.59oE; geomagnetic coordinates: 0.47oN). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 103 electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).

VL  - 12
IS  - 3
ER  -

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Author Information

Sani Abubakar

Department of Physics, Bayero University, Kano, Nigeria; Department of Physical and Life Science, National Space Research and Development Agency, Abuja, Nigeria

Contact Email

http://orcid.org/0009-0004-6973-0949
Daniel Okoh

Istituto Nazionale Geofisica e Vulcanologia (INGV), Roma, Italy; United Nations African Regional Centre for Space Science and Technology Education in English (UN-ARCSSTE-E), Ile-Ife, NARDA, Nigeria
Bello Idrith Tijjani

Department of Physics, Bayero University, Kano, Nigeria
Rabia Salihu Said

Department of Physics, Bayero University, Kano, Nigeria
Benjamin Ayantunji Gbenro

Department of Physical and Life Science, National Space Research and Development Agency, Abuja, Nigeria
Enoch O. Elemo

Department of Physical and Life Science, National Space Research and Development Agency, Abuja, Nigeria

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Plain Text BibTeX RIS

APA Style

Abubakar, S., Okoh, D., Tijjani, B. I., Said, R. S., Gbenro, B. A., et al. (2024). A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. International Journal of Astrophysics and Space Science, 12(3), 63-74. https://doi.org/10.11648/j.ijass.20241203.11

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ACS Style

Abubakar, S.; Okoh, D.; Tijjani, B. I.; Said, R. S.; Gbenro, B. A., et al. A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. Int. J. Astrophys. Space Sci. 2024, 12(3), 63-74. doi: 10.11648/j.ijass.20241203.11

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AMA Style

Abubakar S, Okoh D, Tijjani BI, Said RS, Gbenro BA, et al. A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data. Int J Astrophys Space Sci. 2024;12(3):63-74. doi: 10.11648/j.ijass.20241203.11

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@article{10.11648/j.ijass.20241203.11,
  author = {Sani Abubakar and Daniel Okoh and Bello Idrith Tijjani and Rabia Salihu Said and Benjamin Ayantunji Gbenro and Enoch O. Elemo},
  title = {A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data
},
  journal = {International Journal of Astrophysics and Space Science},
  volume = {12},
  number = {3},
  pages = {63-74},
  doi = {10.11648/j.ijass.20241203.11},
  url = {https://doi.org/10.11648/j.ijass.20241203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijass.20241203.11},
  abstract = {This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in ionPrf file format provided by the COSMIC team. The profiles are in netCDF format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00oN, 8.59oE; geomagnetic coordinates: 0.47oN). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 103 electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Neural Network-Based Ionospheric Electron Density Model over an African Equatorial Region Centered in Kano, Nigeria, Using COSMIC Mission Data

AU  - Sani Abubakar
AU  - Daniel Okoh
AU  - Bello Idrith Tijjani
AU  - Rabia Salihu Said
AU  - Benjamin Ayantunji Gbenro
AU  - Enoch O. Elemo
Y1  - 2024/12/30
PY  - 2024
N1  - https://doi.org/10.11648/j.ijass.20241203.11
DO  - 10.11648/j.ijass.20241203.11
T2  - International Journal of Astrophysics and Space Science
JF  - International Journal of Astrophysics and Space Science
JO  - International Journal of Astrophysics and Space Science
SP  - 63
EP  - 74
PB  - Science Publishing Group
SN  - 2376-7022
UR  - https://doi.org/10.11648/j.ijass.20241203.11
AB  - This study focuses on modeling the ionospheric electron density over a 5 degree rectangular African equatorial region centered in Kano, Nigeria, using artificial neural network. The electron density measurements data used in this research were obtained from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) team covering the period of 16 years ranging from 2006 to 2021 through the method of radio occultation. The data is in ionPrf file format provided by the COSMIC team. The profiles are in netCDF format containing ionospheric information including electron density profiles. The data were collected from a 5-degree rectangular African equatorial region centered in Kano, Nigeria (geographic coordinates: 12.00oN, 8.59oE; geomagnetic coordinates: 0.47oN). Computer neural network was used in training the data using the Levenberg Marquardt (LM) algorithm. Results for 22 trained networks demonstrated that the network with nineteen hidden layer neurons gave the most accurate result with root-mean square error (RMSE) of 86 𝗑 103 electrons per cubic centimeter. The criteria was that the best network is the one that gives the highest minimization of RMSE. The nineteenth network was therefore finally used as the neural network (NN) electron density model. The NN model’s hourly binned electron density predictions was compared with those of the NeQuick and International Reference Ionosphere (IRI) models. The result of the comparison illustrated that out of the 423 investigated ionosonde profiles, the NN model performed best in 200 profiles (approximately 47%) followed by the IRI which performed best in 114 profiles (approximately 27%) while the NeQuick performed best in 109 profiles (approximately 26%). The developed NN model was demonstrated to be able to reproduce the different ionospheric features (diurnal variations, seasonal variations, and the long-term changes).

VL  - 12
IS  - 3
ER  -

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