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

Abstract

The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Klobuchar, NeQuickG, BDGIM, GLONASS, IRI-2016, IRI-2012, IRI-Plas, NeQuick2, and GEMTEC Ionospheric Models: A Comparison in Total Electron Content and Positioning Domains

“…Another way to improve ionospheric modeling is through machine learning techniques, whose usage is continuously increasing for global ionosphere modeling [ 37 ]. Such models now overperform classical models (such as IRI) [ 57 ]. These two ways seem to be the ionospheric modeling future.…”

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

“…To evaluate the effectiveness of the models, we have utilized various statistical parameters such as residual error ( r i ), root‐mean‐square error (RMSE), mean absolute error (MAE), standard deviation of residual errors ( σ ), and correlation coefficient ( R ). These parameters are widely recognized and utilized to determine the performance of a model (Shi et al., 2022; Xiong et al., 2021). The equations are as follows: $$\begin{align...$$…”

“…To evaluate the effectiveness of the models, we have utilized various statistical parameters such as residual error (r i ), root-mean-square error (RMSE), mean absolute error (MAE), standard deviation of residual errors (σ), and correlation coefficient (R). These parameters are widely recognized and utilized to determine the performance of a model (Shi et al, 2022;Xiong et al, 2021). The equations are as follows:…”

“…The ionosphere, which is the upper portion of Earth's atmosphere, comprises ionized plasma that undergoes variations in its composition due to factors such as latitude and longitude, local time, season, solar and geomagnetic activity, and other factors. The electromagnetic wave propagating through this dynamic environment suffers a range delay whose magnitude depends on the frequency of the wave and the amount of the total electron content (TEC) (Hajra et al., 2016; Shi et al., 2022). TEC, which is the total number of electrons within a unit‐square‐meter column along a path through the ionopsphere, is a significant parameter characterizing ionospheric variability (Tang et al., 2022).…”

Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data

“…As a nonlinear prediction filter, it can learn empirical knowledge about relationships between the independent inputs and the dependent output through the use of large data sets. In addition, in comparison with other NN models, for example, the long short‐term memory (Shi et al., 2022) and LSTM‐convolution neural network (Ruwali et al., 2021) models, the ANN model is more flexible as it does not require continuous sample data (each sample set must have the same time interval). Besides, the development of high‐performance computing and data infrastructure has made it feasible to fully exploit the potential of the ANN technique.…”