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Author: search.filters.author.Budtho, JirapoomSubject: search.filters.subject.Total electron content

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    Deep learning-based prediction models for the vertical total electron content using GNSS satellite observations
    (2026-08-01)
    Mutasov, Gleb
    ;
    Myint, Lin Min Min
    ;
    Budtho, Jirapoom
    ;
    Perwitasari, Septi
    ;
    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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    Compatibility of Low-Cost GNSS Receivers for Total Electron Content (TEC) Analysis
    (2025-01-01)
    Rana, Bhim Bahadur
    ;
    Supnithi, Pornchai
    ;
    Myint, Lin M.M.
    ;
    Tongkasem, Napat
    ;
    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.