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

Abstract

Maps of the total electron content (TEC) of the ionosphere can be reconstructed using data extracted from global positioning system (GPS) signals. For historic and other sparse data sets, the reconstruction of TEC images is often performed using multivariate interpolation techniques. In this paper, a quantitative comparison of the ability of artificial neural networks (ANN), polynomial fitting and kriging interpolation was carried out in order to model the spatial variations of TEC using GPS data over Iran. These methods are suitable for handling multi-scale phenomena and unevenly distributed data. The observations collected at 25 GPS stations from Iranian permanent GPS network (uniformly spread all over Iran with sampling rate of 30-seconds). Dual frequency carrier phase and code GPS observations were used. A smoothed TEC approach was used for absolute TEC recovery. Evaluation of the methods has been applied with single GPS station in Tehran equipped with ionosonde instrument. The minimum relative error for ANN, polynomial and kriging are 4.37, 6.35, 9.13 % and the maximum relative error are 8.61, 29.06, and 20.14 % respectively. Also root mean square error (RMSE) of 3.7 TECU is computed for ANN method which is less than RMSE of other mentioned methods. The results show that ANN method has higher accuracy and compiles speed than kriging and polynomial. As well as, it is found that polynomial and kriging methods required many computational points in adjustment step.

Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China

“…Therefore, estimated TEC values must reflect the real features of observable phenomena in the ionosphere. Consequently, many studies have attempted to develop regional TEC forecasting models to explore ionospheric variability (Badeke et al., 2018; Elmunim et al., 2017; Habarulema et al., 2007; Krankowski et al., 2005; Razin et al., 2015; Tebabal et al., 2019; Watthanasangmechai et al., 2012). In general, methods used to forecast regional TEC can be divided into two major categories: empirical methods (Badeke et al., 2018; J. Li et al., 2020; Mukesh et al., 2020; Mukhtarov et al., 2014) and statistical methods.…”

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

“…Deep learning methods have been widely used in the prediction of the ionospheric TEC for a single station (Huang et al., 2015; Kaselimi et al., 2020; Ruwali et al., 2021; Srivani et al., 2019; Xiong et al., 2021; Zewdie et al., 2021), for a region (Li et al., 2020; Okoh et al., 2016, 2019, 2020; Razin et al., 2015; Sabzehee et al., 2018; Song et al., 2018; Tebabal et al., 2019; Uwamahoro et al., 2018), and for the globe (Cesaroni et al., 2020; Chen et al., 2019; Liu et al., 2020; Zhukov et al., 2020). TEC prediction models for a single station based on the artificial neural network (ANN) method (Huang et al., 2015; Huang & Yuan, 2014), the long short‐term memory (LSTM) method (Kaselimi et al., 2020; Srivani et al., 2019; Xiong et al., 2021; Zewdie et al., 2021), and the hybrid deep learning method (Ruwali et al., 2021), all have recently shown promising results.…”

Prediction of the EM signal delay in the ionosphere using neural model

“…In addition to that, thanks to its powerful interpolation and generalization capabilities, the neural model provides a better accuracy prediction of TEC value in comparison with the classical statistical models in geographic areas with a sparse distribution of probe stations in the ionosphere. The mentioned characteristics of ANNs for the modelling of the ionosphere and prediction of TEC values were demonstrated in [12][13][14][15][16][17][18]. In [12] a regional TEC model based on ANN has been designed and tested using data sets collected by the Brazilian GPS network covering periods of low and high solar activity.…”

“…In [12] a regional TEC model based on ANN has been designed and tested using data sets collected by the Brazilian GPS network covering periods of low and high solar activity. In [13] a local specific neural model was proposed for the prediction of TEC values above the area in Iran based on MultiLayer Perceptron (MLP) network. Performances of that model were compared with the polynomial fitting and Kriging interpolation.…”