scholarly journals Efficiency of artificial neural networks in map of total electron content over Iran

2015 ◽  
Vol 51 (3) ◽  
pp. 541-555 ◽  
Author(s):  
Mir Reza Ghaffari Razin ◽  
Behzad Voosoghi ◽  
Ali Mohammadzadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Total Electron Content ◽  
Electron Content ◽  
Total Electron ◽  
Artificial Neural

scholarly journals Reconstruction of Storm‐Time Total Electron Content Using Ionospheric Tomography and Artificial Neural Networks: A Comparative Study Over the African Region

Radio Science ◽  
2018 ◽  
Vol 53 (11) ◽  
pp. 1328-1345 ◽  
Author(s):  
Jean Claude Uwamahoro ◽  
Nigussie M. Giday ◽  
John Bosco Habarulema ◽  
Zama T. Katamzi‐Joseph ◽  
Gopi Krishna Seemala
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Total Electron Content ◽  
Electron Content ◽  
African Region ◽  
Total Electron ◽  
Ionospheric Tomography ◽  
Artificial Neural

scholarly journals Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

Space Weather ◽  
2020 ◽  
Vol 18 (9) ◽  
Author(s):  
Daniel Okoh ◽  
John Bosco Habarulema ◽  
Babatunde Rabiu ◽  
Gopi Seemala ◽  
Joshua Benjamin Wisdom ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Total Electron Content ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Total Electron Content ◽  
Time Modeling ◽  
Artificial Neural

scholarly journals Artificial neural network applications in ionospheric studies

1998 ◽  
Vol 41 (5-6) ◽  
Author(s):  
L. R. Cander
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Numerical Models ◽  
Electron Content ◽  
Total Electron ◽  
Ionospheric Physics ◽  
Modern Development ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Electron Density Profiles

The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC). Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

scholarly journals Modeling Australian TEC Maps Using Long-Term Observations of Australian Regional GPS Network by Artificial Neural Network-Aided Spherical Cap Harmonic Analysis Approach

2020 ◽  
Vol 12 (23) ◽  
pp. 3851
Author(s):  
Wang Li ◽  
Dongsheng Zhao ◽  
Yi Shen ◽  
Kefei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Harmonic Analysis ◽  
Total Electron Content ◽  
Electron Content ◽  
Spherical Cap ◽  
Total Electron ◽  
Australian Continent ◽  
Artificial Neural ◽  
Spherical Cap Harmonic Analysis

The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 1016 electron/m2), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia.

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