Study of the Ionospheric Total Electron Content over central India using Artificial Neural Network

Author(s):  
Sumanjit Chakraborty ◽  
Abhirup Datta
2020 ◽  
Vol 12 (23) ◽  
pp. 3851
Author(s):  
Wang Li ◽  
Dongsheng Zhao ◽  
Yi Shen ◽  
Kefei Zhang

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.


Space Weather ◽  
2020 ◽  
Vol 18 (9) ◽  
Author(s):  
Daniel Okoh ◽  
John Bosco Habarulema ◽  
Babatunde Rabiu ◽  
Gopi Seemala ◽  
Joshua Benjamin Wisdom ◽  
...  

2017 ◽  
Vol 17 (2) ◽  
pp. 12-16
Author(s):  
I. Yakubu ◽  
Y. Y. Ziggah ◽  
D. Asafo-Agyei

Positional accuracy in the usage of GPS receiver is one of the major challenges in GPS observations. The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the transmission of signals to the Earth. Therefore, the total electron content is a key parameter in mitigating ionospheric effects on GPS receivers. Many researchers have therefore proposed various models and methods for predicting the total electron content along the signal path. This paper focuses on the use of two different models for predicting the Vertical Total Electron Content (VTEC). Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.  The developed ANN and ANFIS model gave Root Mean Square Error (RMSE) of 1.953 and 1.190 respectively.  From the results it can be stated that the ANFIS is more suitable tool for the prediction of VTEC. Keywords: Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Vertical Total Electron


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