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Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
(2026-08-01)
Mutasov, Gleb
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Myint, Lin Min Min
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Budtho, Jirapoom
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Perwitasari, Septi
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Nishioka, Michi
Ionospheric Total Electron Content (TEC) is a crucial parameter for characterizing the state of the ionosphere and assessing its impact on satellite-based navigation systems and on communication technologies. In equatorial and low-latitude regions, ionospheric irregularities, particularly equatorial plasma bubbles (EPBs), pose significant challenges for satellite navigation and communication due to their capacity to cause rapid TEC fluctuations and signal degradation. Since these effects are especially pronounced during ionospheric and geomagnetic disturbances, making accurate TEC prediction is an essential task for improving the reliability of GNSS-based positioning and space weather applications. This study presents a machine learning-based framework for one-day-ahead prediction of TEC with a 30-min resolution over the magnetic equator and low-latitude regions, with a focus on Southeast Asia. Unlike global models, our approach is tailored to local GNSS observations and directly predicts TEC values along specific satellite-receiver paths, defined by geographic location and satellite visibility. We integrate ionospheric pierce point (IPP) coordinates, geomagnetic indices, and solar activity indicators as features to enhance temporal and spatial forecasting accuracy. To address the nonlinear and nonstationary nature of TEC variations, we investigate and compare three deep learning architectures: a Transformer-based time-series model, a Temporal Kolmogorov–Arnold Network (TKAN), and a Long Short-Term Memory (LSTM). Additionally, the predictions are benchmarked against the empirical IRI-2020 model and a persistence baseline. The results demonstrate that both the Transformer and TKAN models outperform the LSTM and empirical approaches, particularly during different geomagnetic and ionospheric conditions, showing improved robustness and generalization. The proposed framework highlights the potential for accurate, resource-efficient TEC prediction in low-latitude regions and opens a pathway for further improvements by integrating multi-GNSS observations and additional space weather parameters.
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Study on the relationship between Global Positioning System Total Electron Content Anomalies and Earthquake Events in Thailand during Solar Cycle 24
(2025-10-29)
Pansong, Chollada
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Kenpankho, Prasert
This study investigates ionospheric Total Electron Content (TEC) anomalies in relation to earthquake events in Thailand from 2007 to 2020, encompassing Solar Cycle 24. TEC data were obtained from three sources: the Global Positioning System (GPS), the International GNSS Service (IGS), and the International Reference Ionosphere (IRI), and were compared to 473 earthquakes (Mw ≥ 3.0). While earthquake magnitudes below Mw 5.0 did not exhibit a clear correlation, earthquake events of Mw 5.0 or higher reflected in moderate negative correlation coefficients for GPS TEC, IGS TEC, and IRI TEC (-0.495,-0.501, and-0.303, respectively). Furthermore, a positive correlation coefficient (0.611) was found between Mw ≥ 5.0 earthquakes and geomagnetic storms with the Kp index. However, focusing specifically on geomagnetic storms and TEC variations on the day of an earthquake, no significant relationship was detected across GPS, IGS, and IRI data. Nevertheless, further research is needed to clarify the link between seismic activity and TEC fluctuations, potentially through alternative approaches or targeted case studies. This is especially important given the limited number of earthquakes above a magnitude of 5.0 in our study area, which restricts the available sample size.
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Exploring Ionospheric Disturbances Using GNSS: A STEM-Based Investigation of the 2024 Extreme Geomagnetic Storm
(2025-01-01)
Pansong, Chollada
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Buakao, Nitipat
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Keokhumcheng, Thanapon
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Phothila, Pharinya
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Intamas, Patcharin
This study investigates the effectiveness of a STEM-based instructional approach integrating GNSS satellite technology and ionospheric TEC (Total Electron Content) analysis during extreme geomagnetic storms. The objective was to enhance students' conceptual understanding, practical skills, and STEM-related attitudes through interdisciplinary learning activities. The SPACE model (Study, Plan, Analyze, Create, Evaluate) was applied as a pedagogical framework to guide students through real-world TEC anomaly detection using GPS RINEX data and computational tools. The integration of real satellite data and hands-on analysis enabled students to connect theoretical knowledge with real-world phenomena, deepening their engagement and inquiry-based thinking. Pre-and post-test results revealed statistically significant improvements, with scores increasing from a mean of 5 1. 8 (S D = 7. 5 4) to 7 5. 0(S D= 7.42) (p < 0. 000001). Moreover, the STEM Attitude Questionnaire reflected strong positive perceptions, especially in STEM career motivation (x¯=4.20) and perceived value of STEM (x¯= 4.13). These findings highlight the model's potential to foster STEM readiness. Integrating GNSS-based TEC analysis into the STEM framework significantly enhanced students' academic outcomes, practical skills, and STEM attitudes.
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Compatibility of Low-Cost GNSS Receivers for Total Electron Content (TEC) Analysis
(2025-01-01)
Rana, Bhim Bahadur
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Supnithi, Pornchai
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Myint, Lin M.M.
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Tongkasem, Napat
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Budtho, Jirapoom
Although the geodetic GNSS receivers are highly precise, they are inaccessible to every user, especially in remote areas. Therefore, this work aimed to find the reasons that bolster the low-cost GNSS receivers to be used with high resolution over a wide area, instead of geodetic in space weather studies. A comparative analysis was conducted between a low-cost Ublox ZED-F9P GNSS receiver and a geodetic Novatel Propak6 GNSS receiver, focusing on ionospheric parameters such as slant total electron content (STEC), vertical total electron content (VTEC), and the number of satellites tracked using the Global Positioning System (GPS). Additionally, VTEC values were compared with the GIM model. Both receivers exhibited a similar pattern of TEC, with the R2 value of 0.9734 and the root mean square error of 3.4583. The number of satellites tracked by both receivers during the observed periods was also found to be similar. Moreover, the VTEC results obtained from the low-cost GNSS receiver showed compatibility with the GIM model, demonstrating the reliability of the low-cost receiver in comparison to the geodetic GNSS receiver.
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Total electron content prediction using singular spectrum analysis and autoregressive moving average approach
(2022-01-01)
Dabbakuti, J. R.K.Kumar
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Yarrakula, Mallika
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Panda, Sampad Kumar
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Jamjareegulgarn, Punyawi
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Haq, Mohd Anul
Continuous monitoring of ionospheric behavior and subsequent development or improvement of models for the prediction of its parameters with consistent accuracy remains an ongoing challenge. In this sense, an integrated approach by combining the signal extraction technique Singular Spectrum Analysis (SSA) with Autoregressive Moving Average (ARMA) is presented in this work to predict the ionospheric Total Electron Content (TEC) values that are responsible for causing ionospheric delays in the trans-ionospheric signal propagation associated with satellite-based communication, navigation, and timing applications. In general, SSA is a nonparametric spectral estimation procedure that decomposes the signals into interpretable and physically significant components. The observed TEC from two Global Positioning System (GPS) stations across the low latitude Saudi Arabian region are considered during the year 2017 that falls in the descending phase of solar cycle-24. The performance of the proposed hybrid model is evaluated by comparing with the sole estimation from the ARMA model and the observed GPS–TEC dataset for two different geomagnetic conditions: a) the regular geomagnetically quiet period of 15 to 29 December, 2017 (Ap < 24 and Dst > − 30 nT) and b) the geomagnetic storm period from 7 to 9 September, 2017 (Dst min = − 142 nT). The corresponding average Precision, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed SSA–ARMA model predictions are 1.79 TECU, 1.23 TECU, and 13.02%. In contrast, the respective values in the exclusive ARMA model are 2.01 TECU, 1.37 TECU, 14.42% at Oman station. The corresponding values for Magna station are 0.92 TECU, 0.61 TECU, and 10.76% (SSA–ARMA) and 1.01 TECU, 0.75 TECU, and 11.33% (ARMA). The results show an improved computational efficiency with minor improvement in the TEC predictions with the proposed SSA–ARMA method compared to the sole employment of the ARMA model by disregarding the extraneous components.
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Double-thin-shell approach to deriving total electron content from GNSS signals and implications for ionospheric dynamics near the magnetic equator
(2021-12-01)
Maruyama, Takashi
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Hozumi, Kornyanat
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Ma, Guanyi
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Supnithi, Pornchai
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Tongkasem, Napat
A new technique was developed to estimate the ionospheric total electron content (TEC) from Global Navigation Satellite System (GNSS) satellite signals. The vertically distributed electron density was parameterized by two thin-shell layers (double-shell approach). The spatiotemporal variation of TEC (strictly speaking, partial electron content) associated with each shell was approximated by the functional fitting of spherical surface harmonics. The major improvements over the conventional single-shell approach were as follows: (1) the precise estimation of TEC was achieved; (2) the estimated TEC was less dependent on the choice of shell heights; and (3) the equatorial anomaly was captured more correctly. Furthermore, higher and lower shells exhibited a different pattern of local time vs latitude variation, providing information on the ionosphere–thermosphere dynamics. [Figure not available: see fulltext.]
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Empirical orthogonal function modelling of total electron content over Nepal and comparison with global ionospheric models
(2020-12-01)
Jamjareegulgarn, Punyawi
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Ansari, Kutubuddin
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Ameer, Afsheen
This analysis uses 3 years hourly observations of total electron content (TEC) from 5 global positioning system (GPS) station across Nepal to analyze the spatial trend. For this purpose, empirical orthogonal function modeling is used to investigate the annual and monthly variation inside the country. Empirical orthogonal function base functions and associated coefficients of TEC variability over Nepal have been studied to establish the relationship between observed and modeled TEC values. Both the observed GPS TEC values and the empirical orthogonal function modeled TEC values are compared with the global ionospheric models (global ionospheric map and international reference ionosphere) TEC values. The study shows an hourly pattern of TEC variation in which the TEC rises from dawn, reaches the highest TEC values about 40 TEC units during the peak hours of the day, then decreases at evening at the lowest diurnal values about 5 TEC units. Monthly TEC values over all sites are higher during the march and April (about 38 TEC unit), while they are lower values during December and January (about 10 TEC unit). Although the correlation coefficients between the GPS TEC values and the global modeled TEC values are higher, while it becomes highest with empirical orthogonal function modeled TEC values for both cases of hourly and monthly variations. We examined the root mean square errors between observed and modeled TEC values at each site by using tailor correlation plots and found that they are lower in case of empirical orthogonal function model. Monthly residuals between observed and empirical orthogonal function modeled TEC values are always lower as compared to other global modeled TEC values. These kinds of comparative analysis in the present work indicate that empirical orthogonal function model by using global geomagnetic activity works very well and is capable of depicting TEC variations accurately.

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