Total Electron Content Prediction Research Articles

Prediction of ionospheric TEC during the occurrence of earthquakes in Indonesia using ARMA and CoK models

Predicting ionospheric Total Electron Content (TEC) variations associated with seismic activity is crucial for mitigating potential disruptions in communication networks, particularly during earthquakes. This research investigates applying two modelling techniques, Autoregressive Moving Average (ARMA) and Cokriging (CoK) based models to forecast ionospheric TEC changes linked to seismic events in Indonesia. The study focuses on two significant earthquakes: the December 2004 Sumatra earthquake and the August 2012 Sulawesi earthquake. GPS TEC data from a BAKO station near Indonesia and solar and geomagnetic data were utilized to assess the causes of TEC variations. The December 2004 Sumatra earthquake, registering a magnitude of 9.1–9.3, exhibited notable TEC variations 5 days before the event. Analysis revealed that the TEC variations were weakly linked to solar and geomagnetic activities. Both ARMA and CoK models were employed to predict TEC variations during the Earthquakes. The ARMA model demonstrated a maximum TEC prediction of 50.92 TECU and a Root Mean Square Error (RMSE) value of 6.15, while the CoK model predicted a maximum TEC of 50.68 TECU with an RMSE value of 6.14. The August 2012 Sulawesi earthquake having a magnitude of 6.6, revealed TEC anomalies 6 days before the event. For both the Sumatra and Sulawesi earthquakes, the GPS TEC variations showed weak associations with solar and geomagnetic activities but stronger correlations with the earthquake-induced electric field for the considered two stations. The ARMA model predicted a maximum TEC of 54.43 TECU with an RMSE of 3.05, while the CoK model predicted a maximum TEC of 52.90 TECU with an RMSE of 7.35. Evaluation metrics including RMSE, Mean Absolute Deviation (MAD), Relative Error, and Normalized RMSE (NRMSE) were employed to assess the accuracy and reliability of the prediction models. The results indicated that while both models captured the general trend in TEC variations, nuances emerged in their responses to seismic events. The ARMA model demonstrated heightened sensitivity to seismic disturbances, particularly evident on the day of the earthquake, whereas the CoK model exhibited more consistent performance across pre- and post-earthquake periods.

Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms.

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

An accurate prediction of ionospheric total electron content (TEC) at the primary stage is essential for applications related to global navigation satellite systems (GNSS) under varying weather conditions. The previous TEC prediction schemes contribute for each time step that increases the prediction time. The eye contact phenomenon establishes a metaphorical connection which intends to capture and emphasize the attention worthy elements in a sequence. This research introduces a deep learning approach which is a combination of attention-based bidirectional long short-term memory and gated recurrent unit (Bi-LSTM GRU) to predict TEC in the ionosphere. Bidirectional LSTM is the better option for achieving durability when combined with a gated recurrent unit (GRU) to predict TEC in the ionosphere. The proposed approach is evaluated with the existing LSTM approach for root mean square error (RMSE) during training and validation. The RMSE while predicting the global ionospheric delay using the existing LSTM for 20 epochs is seen to be 0.004, whereas the existing approach achieves a training error of 0.003.

A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression

Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.

Global ionospheric total electron content short-term forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997–2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model

The Ionosphere, a crucial region of Earth’s atmosphere extending from approximately 50 to 1000 km above the Earth’s surface, plays a pivotal role in global communication, navigation, and satellite-based technologies. Among its various parameters, Total Electron Content (TEC) stands out as a key metric, representing the integrated electron density along a specific path through the ionosphere. This research focuses on analyzing TEC variations during Indonesian earthquakes between 2004 and 2024 using a Recurrent Neural Network (RNN) model. The study investigates the ionospheric response to seismic activity, particularly in Indonesia, a region prone to earthquakes due to its location within the Pacific Ring of Fire and complex tectonic setting involving multiple lithospheric plates. The RNN model, designed to predict TEC variations during earthquakes, utilizes data on solar and geomagnetic activity, including the Kp index, solar wind, and geomagnetic storm activity, obtained from the OMNIWEB data centre, along with TEC data from the IONOLAB data servers for the BAKO station in Cibinong, Indonesia. The earthquakes considered for observation include significant events such as the 2004 Indian Ocean earthquake and tsunami, the 2012 Wharton Basin earthquake, and subsequent seismic events up to 2024. Statistical metrics such as Mean Bias Deviation (MBD), Relative Error (REL_E), Absolute Error (ABS_E), and Root Mean Square Error (RMSE) are used to evaluate the predictive capability of the RNN model and compare it with the International Reference Ionosphere (IRI) 2020 model, a widely accepted empirical model for describing ionospheric parameters. The comparison reveals the performance of the RNN model in capturing TEC variations during seismic events, providing valuable insights for earthquake monitoring and early warning systems.

Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

AbstractAn accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters Wprot (a measure of the ionospheric disturbance during storm time) and Econv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid UQRG global ionosphere maps (GIMs). Data from the years 2015 and 2020 were excluded from the training data set and used for testing. The performance of the LSTM‐based TEC model is tested for near real‐time (RT) cases as well by using RT products (IRTG GIMs) as historical TEC inputs. We compared the performance of the LSTM‐based model to a quiet‐time feed forward neural network (FNN)‐based model and the Neustrelitz TEC model (NTCM). The results indicate that the LSTM‐based model proposed here is outperforming the FNN‐based model and NTCM in both cases, that is, using the UQRG or the IRTG GIMs as input for the historical TEC.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models.

Prediction of ionospheric total electron content over low latitude region: Case study in Ethiopia

This study mainly focuses on the prediction of the variability of the Total Electron Content (TEC) affecting space and ground-based technological infrastructures vulnerable to extreme space weather events. The impact of solar wind conditions (SWC) can be quantified by measuring the ionosphere’s TEC. We used a machine learning (ML) algorithm based on the Support Vector Machine (SVM), Long Short-Term Memory (LSTM) Neural Network (NN), and International Reference Ionosphere 2016 (IRI 2016) models to predict the hourly TEC over Ethiopia during quiet and storm geomagnetic conditions. We considered TEC data from the Global Positioning System (GPS) stations at ADIS, CURG, and NEGE during the years 2013 to 2016. The results indicated that the SVM model predicted the hourly TEC variation with a root mean square error (RMSE) between 2.136 and 7.923 Total Electron Content Unit (TECU) under different ionospheric conditions. For the LSTM model, the RMSE between the predicted and observed TEC lies between 1.483 and 2.527 TECU. But the empirical IRI 2016 models’ RMSE from GPS TEC is in the range of 4.777 and 14.519 TECU. The statistical study points out the LSTM has the highest prediction accuracy and is the most robust for accurate prediction of ionospheric TEC in comparison to the SVM and IRI 2016 models throughout both storm and quiet periods. The result can be further improved with a lower RMSE (high correlation) by utilizing auxiliary data and increasing the number of stations employed in the study. This research adds fresh insights to deep learning applications in space weather forecasting in the region of interest.

Prediction of ionospheric TEC using a GRU mechanism method

In this paper, we propose a gate recurrent unit (GRU) method to forecast ionospheric the total electron content (TEC) at different latitudes and longitudes. This method aims to address the issues of low accuracy in traditional neural networks and the challenges faced in training long-term time series. We construct a GRU model using TEC data from the European Orbit Determination Center (CODE) for the years 2016 to 2018, geomagnetic indices, and solar activity indices (16 points along the latitude and longitude lines of 120°E and 30°N). Comparative experiments are carried out with the international reference ionosphere (IRI) model and the long short-term memory (LSTM) model. The results show that the GRU model outperforms other models in terms of accuracy. On geomagnetic quiet days, the 5-day predicted average residual can reach 0.51 TECU, while on storm days it can reach 0.72 TECU. The root mean square error (RMSE) for these days is 0.64 TECU and 1.01 TECU, respectively. The correlation coefficients are 0.98 and 0.93, respectively. The RMSE of the GRU model ranges from 0.60 TECU to 2.62 TECU for both geomagnetic quiet days and storm days. In terms of TEC forecast accuracy in 2018, the RMSE of the GRU model is 1.6 TECU, which is smaller than that of the IRI model and LSTM model. These results demonstrate that the GRU model performs better than the IRI model and LSTM model.

Ionospheric TEC Prediction Based on Ensemble Learning Models

AbstractIn this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1‐hr TEC prediction at high‐ (80°W, 80°N), mid‐ (80°W, 40°N), and low‐ latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high‐ and mid‐ latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1‐day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.

Prediction of ionospheric total electron content data using NARX neural network model

Successful prediction of ionospheric total electron content (TEC) data will help in correction of positioning errors in global navigation satellite systems (GNSS) caused by the ionosphere. This research paper proposes a prediction model for ionospheric TEC using a nonlinear autoregressive with exogenous inputs (NARX) neural network that utilizes past TEC data alongwith solar and geomagnetic indices namely F10.7, disturbed storm (Dst), Kp, Ap, and time of the day. We assess the prediction capability of our model at different latitudes during different solar activity years. We compare our results with another NARX model which uses previous TEC data along with time of the day, day of the year and season as exogenous parameters. The results show that for the solar minimum year the TEC prediction accuracy improves by 35.71% and for the solar maximum year it improves by 31.20%. The results using root mean square error (RMSE), mean absolute error (MAE), correlation coefficient, and symmetric mean absolute percentage error (sMAPE) clearly indicate that solar and geomagnetic indices along with time of the day help in enhancing prediction accuracy of TEC across different latitudinal regions during both solar minimum and maximum years.

Forecasting of Global Ionosphere Maps With Multi‐Day Lead Time Using Transformer‐Based Neural Networks

AbstractIonospheric total electron content (TEC) is a key indicator of the space environment. Geophysical forcing from above and below drives its spatial and temporal variations. A full understanding of physical and chemical principles, available and well‐representable driving inputs, and capable computational power are required for physical models to reproduce simulations that agree with observations, which may be challenging at times. Recently, data‐driven approaches, such as deep learning, have therefore surged as means for TEC prediction. Owing to the fact that the geophysical world possesses a sequential nature in time and space, Transformer architectures are proposed and evaluated for sequence‐to‐sequence TEC predictions in this study. We discuss the impacts of time lengths of choice during the training process and analyze what the neural network has learned regarding the data sets. Our results suggest that 12‐layer, 128‐hidden‐unit Transformer architectures sufficiently provide multi‐step global TEC predictions for 48 hr with an overall root‐mean‐square error (RMSE) of ∼1.8 TECU. The hourly variation of RMSE increases from 0.6 TECU to about 2.0 TECU during the prediction time frame.

Performance analysis of FFNN and Kriging model on prediction of ionospheric TEC during April 2022 – X 1.1 solar flare

Artificial Intelligence (AI) in data analysis has become an integral tool in recent times. In this research, Total Electron Content (TEC) prediction by the Feed Forward Neural Network (FFNN) model is compared with the Ordinary Kriging based Surrogate Model (OKSM) to verify the significance of the sample size required for FFNN and OKSM. To assess the credibility of the constructed models, the FFNN and OKSM prediction models were evaluated on 30 April 2022, during which a Solar Flare (SF) of intensity X 1.1 occurred. The TEC data is taken from Hyderabad (17.31°N and 78.55°E) from IONOLAB data servers. The solar parameters were collected from the NASA OMNIWEB data server. The surrogate model is built to predict the seventh-day TEC values by using the previous six days of TEC data, Whereas the FFNN model uses 146 days of TEC data as training data set to predict the subsequent four days of TEC. The performance of the models is evaluated using statistical parameters like Root Mean Square Error (RMSE), Correlation Coefficient (CC), Mean Absolute Error (MAE) and symmetric Mean Absolute Percentage Error (sMAPE). The results were represented as linear regression scatter plots, showing fewer residuals for the constructed prediction models.

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

AbstractThis study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.

The capability of Artificial Neural Networks as a Model for Predicting Total Electron Content (TEC): A Review

The results of investigations from a complete analysis of ANN application on Total Electron Content (TEC) prediction are presented in this paper. TEC is important in defining the ionosphere and has many everyday applications, for example, satellite navigation, time delay and range error corrections for single frequency Global Positioning System (GPS) satellite signal receivers. The total electron content (TEC) in the ionosphere has been measured using GPS. GPS are not installed in every point on the earth to make global TEC measurements possible. As a result, it is crucial to have certain models that can aid to get data from places where there is not any in order to comprehend the global behavior of TEC. Neural Network (NN) models have been shown to accurately anticipate data patterns, including TEC. The capacity of neural networks to represent both linear and nonlinear relationships directly from the data being modeled is what makes them so powerful. The survey from literature reveals that, Levenberg-Marquardt algorithm is preferred and used mostly because of its speed and efficiency during learning process, and that ANN showed a good prediction of TEC compared to the IRI model. As a result, NNs are suitable for forecasting GPS TEC values at various locations if the model's input parameters are well specified.

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

AbstractIn order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast.

Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model

AbstractThis paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate‐features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short‐term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM‐ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1‐day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM‐ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM‐ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio‐temporal data association for a long time, and realize high precision of prediction performance.

Ionospheric TEC prediction using FFNN during five different X Class solar flares of 2021 and 2022 and comparison with COKSM and IRI PLAS 2017

Abstract The Ionospheric Total Electron Content (TEC) measured in the ray path of the signals directly contributes to the Range Error (RE) of the satellite signals, which affects positioning and navigation. Employing the Co-Kriging-based Surrogate Model (COKSM) to predict TEC and RE correction has proven prolific. This research attempted to test and compare the prediction capability of COKSM with an Artificial Intelligence-based Feed Forward Neural Network model (FFNN) during five X-Class Solar Flares of 2021–22. Also, the results are validated by comparing them with the IRI PLAS 2017 model. TEC, solar, and geomagnetic parameters data for Hyderabad GPS station located at 17.31° N latitude and 78.55° E longitude were collected from IONOLAB & OMNIWEB servers. The COKSM uses six days of input data to predict the 7th day TEC, whereas prediction using the FFNN model is done using 45 days of data before the prediction date. The performance evaluation is done using RMSE, NRMSE, Correlation Coefficient, and sMAPE. The average RMSE for COKSM varied from 1.9 to 9.05, for FFNN it varied from 2.72 to 7.69, and for IRI PLAS 2017 it varied from 7.39 to 11.24. Likewise, evaluation done for three different models over five different X-class solar flare events showed that the COKSM performed well during the high-intensity solar flare conditions. On the other hand, the FFNN model performed well during high-resolution input data conditions. Also, it is notable that both models performed better than the IRI PLAS 2017 model and are suitable for navigational applications.