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Author: search.filters.author.Budtho, JirapoomSubject: search.filters.subject.LSTM

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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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    Comparative Analysis of Deep Learning Models for Daily Solar Indices Forecasting in Solar Cycle 25
    (2025-01-01)
    Min Myint, Lin Min
    ;
    Mutasov, Gleb
    ;
    Supnithi, Pornchai
    ;
    Budtho, Jirapoom
    Accurate forecasting of solar activity indices, particularly the Sunspot Number (SSN) and the F10.7 solar radio flux index (F10.7), is essential for effective space weather monitoring, as severe solar and ionospheric disturbances can significantly impact satellite operations, radio communications, and navigation systems. This paper presents a comparative analysis of deep learning models - Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and encoder-only Transformer architectures - for daily forecasting of SSN and F10.7 up to 14 days ahead based on past 27 days. Considering relatively simple model structures, both single-step and multi-step prediction strategies are explored to evaluate the models' capability in handling short-and long-term dependencies in time series data. Daily solar activity data spanning seven solar cycles (Cycles 19-25), obtained from the GFZ Helmholtz Centre for Geosciences, are used for model training and evaluation. Experimental results show that LSTM consistently achieves the best performance across most forecast horizons, particularly in short-to medium-term predictions. The Transformer model delivers competitive and stable results, while TCN performs relatively less effectively, indicating the need for more complex architecture and optimization strategies. These findings highlight the strengths and limitations of each architecture for solar activity forecasting applications.
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    Predicting Equatorial Ionospheric Total Electron Content Using the Transformer-based Model with Observations From Ground GNSS Receivers and COSMIC-2 Satellites
    (2025-01-01)
    Mutasov, Gleb
    ;
    Supnithi, Pornchai
    ;
    Budtho, Jirapoom
    ;
    Perwitasari, Septi
    ;
    Nishioka, Michi
    Ionospheric Total Electron Content (TEC) is a key parameter for monitoring and studying the ionosphere, which induces significant delays in radio signals. Equatorial ionospheric irregularities, such as Equatorial plasma bubbles (EPB), can severely disrupt satellite navigation and communication. Predicting TEC is, therefore, essential for space weather monitoring and high-precision positioning applications. This study employs a Transformer-based model to predict TEC 24 hours in advance for specific satellites based on observations from a ground station and COSMIC-2 satellites. Unlike other approaches, our model directly forecasts TEC values for visible satellites within predefined longitude-latitude ranges. To enhance predictive accuracy, we also integrate additional features: ionospheric pierce points (IPP), geomagnetic (HP60), and solar activity indices, utilizing time-series Transformer architecture, and we consider a long-short-term memory (LSTM) model as a baseline. The proposed approach provides promising results for local TEC forecasting in the specific coverage area, with potential for further enhancements using additional GNSS or TEC measurements.