Budtho, Jirapoom

Preferred name

Budtho, Jirapoom

Alternative Name

Budtho, J.

Main Affiliation

School of Engineering

jirapoom.bu@kmitl.ac.th

Now showing 1 - 7 of 7

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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
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Mutasov, Gleb
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Supnithi, Pornchai
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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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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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Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
(2025-01-01)
Mutasov, Gleb
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
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Nishioka, Michi
Equatorial ionospheric irregularities, particularly those associated with equatorial plasma bubbles (EPB), can significantly disrupt satellite navigation and communication systems. As the demand for reliable Global Navigation Satellite System (GNSS) and communication services grows, the prediction of ionospheric irregularities becomes critical. A key step in the prediction process is to identify distinct spatiotemporal patterns of irregularities, including day-to-day, longitudinal, and seasonal variations. However, with large datasets, manually classification or identification of these irregularities is a complex and challenging task. In this work, we propose unsupervised machine learning techniques to recognize and group irregularity patterns in large, unlabeled Rate of Total Electron Content (TEC) Index (ROTI) keograms. Specifically, two machine learning models: Gaussian Mixture Model and k-means clustering are employed. The ROTI keograms are constructed using GNSS data from two low-latitude receiver stations in Thailand. To reduce redundancy in the keogram images, three feature extraction techniques are applied before the clustering process. A comparative analysis is performed to determine the optimal number of clusters using these models. Based on the results, the optimal combination of feature extraction and clustering technique is determined for the proposed clustering model. The resulting k-means model with contour extractor classifies five distinct patterns of ionospheric irregularity patterns, providing valuable insights for enhancing EPB prediction models and deepening our understanding of ionospheric dynamics. Furthermore, these five irregularity patterns are analyzed in relation to space weather parameters such as the solar radio flux index (F10.7), and the geomagnetic index (Kp). The findings contribute to the development of robust prediction models, improving the reliability of satellite-based communication and navigation systems.
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Thermosphere–Ionosphere Responses Over Thailand During the 2015 St. Patrick's Day Storm: Comparison of Observed O/N2 and VTEC With the SD WACCM-X Model Outputs
(2025-10-01)
Jamlongkul, Paparin
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Wannawichian, Suwicha
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Paxton, Larry J.
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Cantrall, Clayton E.
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Liu, Han Li
We present the first comparative analysis of observational data and model results focusing on thermospheric-ionospheric responses over the Thailand region by studying the St. Patrick's Day geomagnetic storm on 17–18 March, 2015. This study aims to advance our understanding of regional responses by building on previous observation-model comparisons. The observational data include global O/N<inf>2</inf> ratios from GUVI onboard the TIMED spacecraft, global vertical total electron content (VTEC) from the worldwide GNSS receivers obtained from the Madrigal database, and regional VTEC over Thailand from the KMI6 GNSS station. The atmospheric simulations used are from SD WACCM-X, incorporating high-latitude drivers from the Weimer and Assimilative Mapping of Ionospheric Electrodynamics (AMIE) models. The O/N<inf>2</inf> comparison focuses on TIMED's overpasses across Thailand at 3 UT (10 LT) on both days. Both models tend to reproduce general trends in the O/N<inf>2</inf> ratio and VTEC variations prior to the storm onset. The SD WACCM-X/Weimer model shows better agreement with the O/N<inf>2</inf> ratio from GUVI observations over Thailand, particularly during the recovery phase. Meanwhile, the SD WACCM-X/AMIE model better captures VTEC trends on both large and localized scales, especially after sunset, and successfully reproduces localized features over Thailand. However, during the early recovery phase, both Weimer and AMIE drivers fail to fully capture the collapse of the equatorial ionospheric anomaly (EIA) as indicated by VTEC data, likely due to overestimated (Formula presented.) drift values at low latitudes.
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Impact of ionospheric disturbances on NIC and NACp degradation in ADS-B messages
(2025-01-01)
Takahashi, Toru
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Pongpeaw, Anurak
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Saito, Susumu
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Koga, Tadashi
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Supnithi, Pornchai
Southern Japan is located in the low geomagnetic latitude region, where amplitude scintillations associated with equatorial plasma bubbles are often observed. The Electronic Navigation Research Institute (ENRI) has installed GNSS scintillation receivers and an all-sky camera on Ishigaki Island to monitor ionospheric disturbances. The GNSS receivers used are Septentrio Pola5S, which are also utilized for the Ground Based Augmentation System (GBAS) at New Ishigaki Airport (24.4 deg. N, 124.2 deg. E), which is the southernmost airport with regular flights in Japan. The all-sky camera can capture ionospheric disturbances, such as plasma bubbles. The ADS-B receiver has also been installed at Ishigaki Island and received its message within almost 150 NM. The ADS-B observation on Ishigaki Island has been operational since 2023. We observed that plasma bubbles and degradations in NIC and NACp values occurred simultaneously on March 16, 2024. We calculated the Ionospheric Pierce Points (IPP) of GPS satellites observed by aircraft showing degraded NIC and NACp values. Plasma bubbles captured by the all-sky camera were projected onto the map. One GPS satellite's IPP from the flight, which sent those degraded values, was located at the edge of a plasma bubble, and the potential impact of the plasma bubble was considered. However, same analyses were conducted on two other satellites flying in the vicinity, and although these two flights were in conditions that were either equivalent to or more susceptible to the effects of the plasma bubble, neither the NIC nor NACp showed any degradation. Therefore, Instead, radio frequency interference (RFI), or equipment failure is likely one of the causes of these degradations.
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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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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
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Supnithi, Pornchai
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Budtho, Jirapoom
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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.

Budtho, Jirapoom

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