PREDICTION OF TOTAL ELECTRON CONTENT OF THE IONOSPHERE USING NEURAL NETWORK

2016 ◽  
Vol 78 (5-8) ◽  
Author(s):  
Mariyam Jamilah Homam
Keyword(s):  
Neural Network ◽  
Total Electron Content ◽  
Relative Error ◽  
Absolute Error ◽  
Ionospheric Scintillation ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Total Electron ◽  
Acceptable Range ◽  
Set Up

This paper presents the prediction of hourly Vertical Total Electron Content (VTEC) using a neural network by utilizing the data from a GPS Ionospheric Scintillation and TEC Monitor (GISTM) receiver for six years (from 2005 to 2010) during low to medium solar activity (Sunspot number (SSN) between 0.0 and 42.6). Several network configurations were investigated to observe the effect of the number of neurons, and hidden layers. Overall testing process for several network set-up yielded Root Mean Square Error (RMSE) value of 3 to 7 TECU, absolute error of 2 to 6 TECU and relative error of 8% to 28%.  Testing using April 2010 to November 2010 data (SSN from 8.0 to 25.2) produced RMSE value of 2.95 to 3.88 TECU,absolute error of 2.39 to 3.09 TECU and relative error of 8.11% to 16.18%, which are within the acceptable range. 

scholarly journals Appraisal of ANN and ANFIS for Predicting Vertical Total Electron Content (VTEC) in the Ionosphere for GPS Observations

2017 ◽  
Vol 17 (2) ◽  
pp. 12-16
Author(s):  
I. Yakubu ◽  
Y. Y. Ziggah ◽  
D. Asafo-Agyei
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Total Electron Content ◽  
Fuzzy Inference ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Inference System ◽  
Total Electron ◽  
Neuro Fuzzy ◽  
Gps Observations

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

Analysis of ionospheric vertical total electron content before the 1 April 2014 Mw 8.2 Chile earthquake

2017 ◽  
Vol 21 (6) ◽  
pp. 1599-1612 ◽  
Author(s):  
Weiping Jiang ◽  
Yifang Ma ◽  
Xiaohui Zhou ◽  
Zhao Li ◽  
Xiangdong An ◽  
...  
Keyword(s):  
Total Electron Content ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Total Electron ◽  
Chile Earthquake

scholarly journals RESPON TEC IONOSFER DI ATAS BANDUNG DAN MANADO TERKAIT FLARE SINAR-X MATAHARI KELAS M5.1 DAN M7.9 TAHUN 2015 (IONOSPHERIC TEC RESPONSE OVER BANDUNG DAN MANADO ASSOCIATED WITH M5.1 AND M7.9 CLASSES OF SOLAR FLARE XRAYS IN 2015)

2018 ◽  
Vol 14 (2) ◽  
pp. 111
Author(s):  
Sri Ekawati
Keyword(s):  
Time Delay ◽  
Solar Flare ◽  
Electron Density ◽  
Total Electron Content ◽  
Geomagnetic Latitude ◽  
Ionospheric Scintillation ◽  
Electron Content ◽  
Ionospheric Disturbances ◽  
X Rays ◽  
Total Electron

The solar flare is potential to cause sudden increase of the electron density in the ionosphere,particularly in D layer, known as Sudden Ionospheric Disturbances (SID). This increase of electron density occurs not only in the ionospheric D layer but also in the ionospheric E and F layers. Total Electron Content (TEC) measured by GPS is the total number of electrons from D to F layer. The aim of this research is to study the effect of solar flare x-rays, greater than M5 class in 2015, on ionospheric TEC over Bandung and Manado. This paper presents the preliminary result of ionospheric TEC response on solar flare occurrence over Indonesia. The ionospheric TEC data is derived from GPS Ionospheric Scintillation and TEC Monitor (GISTM) receiver at Bandung (-6.90o S;107.6o E geomagnetic latitude 16.54o S) and Manado (1.48o N; 124.85o E geomagnetic latitude 7.7o S). The solar x-rays flares classes analyzed where M5.1 on 10 March 2015 and M7.9 on 25 June 2015. Slant TEC (STEC) values where calculated to obtain Vertical TEC (VTEC) and the Differential of the VTEC (DVTEC) per PRN satellite for further analysis. The results showed that immediately after the flare, there where sudden enhancement of the VTEC and the DVTEC (over Bandung and Manado) at the same time. The time delay of ionospheric TEC response on M5.1 flare was approximately 2 minutes, then the VTEC increased by 0.5 TECU and the DVTEC rose sharply by 0.5 – 0.6 TECU/minutes. Moreover, the time delay after the M7.9 flare was approximately 11 minutes, then the VTEC increased by 1 TECU and the DVTEC rose sharply by 0.6 – 0.9 TECU/minutes. ABSTRAK Flare matahari berpotensi meningkatkan kerapatan elektron ionosfer secara mendadak, khususnya di lapisan D, yang dikenal sebagai Sudden Ionospheric Disturbances (SID). Peningkatan kerapatan elektron tersebut terjadi tidak hanya di lapisan D, tetapi juga di lapisan E dan F ionosfer. Total Electron Content (TEC) dari GPS merupakan jumlah banyaknya elektron total dari lapisan D sampai lapisan F. Penelitian ini bertujuan mengetahui efek flare, yang lebih besar dari kelas M5 tahun 2015, terhadap TEC ionosfer di atas Bandung dan Manado. Makalah ini merupakan hasil awal dari respon TEC ionosfer terhadap fenomena flare di atas Indonesia. Data TEC ionosfer diperoleh dari penerima GPS Ionospheric Scintillation and TEC Monitor (GISTM) di Bandung (-6,90o S; 107,60o E lintang geomagnet 16,54o LS) dan Manado (1,48oLU;124,85oBT lintang geomagnet 7,7o LS) dikaitkan dengan kejadian flare kelas M5.1 pada tanggal 10 Maret 2015 dan kelas M7.9 pada tanggal 25 Juni 2015. Nilai Slant TEC (STEC) dihitung untuk memperoleh nilai Vertical TEC (VTEC), kemudian nilai Differential of VTEC (DVTEC) per PRN satelit diperoleh untuk analisis selanjutnya. Hasil menunjukkan segera setelah terjadi flare, terjadi peningkatan VTEC dan DVTEC (di atas Bandung dan Manado) secara mendadak pada waktu yang sama. Waktu tunda dari respon TEC ionosfer setelah terjadi flare M5.1 adalah sekitar 2 menit, kemudian VTEC meningkat sebesar 0,5 TECU dan DVTEC meningkat secara tajam sebesar 0,5 – 0,6 TECU/menit. Sedangkan, waktu tunda setelah terjadi flare M7.9 adalah 11 menit, kemudian VTEC meningkat sebesar 1 TECU dan DVTEC meningkat secara tajam sebesar 0,6 – 0,9 TECU/menit.

scholarly journals Neural network based model for global Total Electron Content forecasting

2020 ◽  
Vol 10 ◽  
pp. 11 ◽  
Author(s):  
Claudio Cesaroni ◽  
Luca Spogli ◽  
Angela Aragon-Angel ◽  
Michele Fiocca ◽  
Varuliator Dear ◽  
...  
Keyword(s):  
Neural Network ◽  
Total Electron Content ◽  
Geomagnetic Storms ◽  
Single Point ◽  
Global Scale ◽  
External Input ◽  
Electron Content ◽  
International Gnss Service ◽  
Total Electron ◽  
Grid Points

We introduce a novel empirical model to forecast, 24 h in advance, the Total Electron Content (TEC) at global scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 h single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC (Universitat Politècnica de Catalunya) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE, as it has been found to range between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-h forecasting technique even over the oceans. The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.

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