Budtho, Jirapoom

Preferred name

Budtho, Jirapoom

Alternative Name

Budtho, J.

Main Affiliation

School of Engineering

jirapoom.bu@kmitl.ac.th

Now showing 1 - 10 of 20

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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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Radio Frequency Interference (RFI) Analysis on GNSS Signals and Effects on Positioning Errors
(2024-01-01)
Sophan, Somkit
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Supnithi, Pornchai
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Myint, Lin M.M.
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Budtho, Jirapoom
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Saito, Susumu
Radio frequency interference (RFI) frequently exists in the Global Navigation Satellite System (GNSS) signals. Conventionally, the monitoring of carrier-to-noise density ratio (C/N0) values can be used to detect the RFI levels. Since RFI affects the quality of GNSS signals and applications, we determine three RFI levels (low, moderate, and high) based on C/N0 then statistically analyze the RFI occurrences in Bangkok, Thailand. The percentage of RFI occurrences is computed based on the average C/N0 technique. We compare the RFI occurrence statistics of 2 GNSS stations in the urban (DPT9) and suburban (KMIT) areas in Bangkok in June 2021 (COVID-19 lockdown period) and 2023 (Post COVID-19 period). The results show that during the COVID-19 period, the RFI occurrences are much reduced at the DPT9 station in the center of the city. The high (severe) RFI levels at DPT9 (urban) are mostly higher than those at KMIT station (suburban). In addition, we investigate the effects of high (severe) RFI events on single-point and real-time kinematics (RTK) positioning errors.
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The Improvement of Time-step method for Ionospheric Delay gradient Estimation
(2019-06-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saito, Susumu
Ground-Based Augmentation System (GBAS) is concerned with the aircraft positioning correction for precision landing. The ionospheric irregularity is a major impediment of the performance of GBAS. When an airport has at least 2 Global Navigation Satellite System (GNSS) stations, the ionospheric delay gradients can be obtained by using the dual-frequency GNSS stations. If there is only 1 GNSS station in the area, the time step-method can be used. Normally, the baseline from the time-step method is varied according to the sampling rate of GNSS data. Although the sampling rate of the GNSS receiver is a constant value, the baseline varies due to the speed of the GNSS satellite, observed from the ground reference, which can be up to 20 difference km. This paper proposes the dynamic Time-Step method which designates the baseline by adjusting the time between 2 snapshots. The results show that the range of baseline from the reference time-step method, which is set to 1 minute of the sampling rate, can be reduced to 0.5-km variation.
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Single-Frequency Time-Step Ionospheric Delay Gradient Estimation at Low-Latitude Stations
(2020-01-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saito, Susumu
The irregularity of the local-area ionospheric delay is a primary impediment for Ground-Based Augmentation System (GBAS) services. Excessive ionospheric delay gradients may degrade aircraft positioning for high precision landing systems. Therefore, the spatial gradients of the nominal background ionosphere must be studied as their statistics will be sent to the approaching aircraft. For the well-known station-pair method, ionospheric delay gradient estimation requires at least 2 Global Navigation Satellite System (GNSS) reference stations. This method can be applied to both single or dual-frequency GNSS receivers. However, when the GNSS stations are far apart, it is not suitable for estimating the ionospheric delay gradients at short baselines, and the time-step method is an attractive alternative. In this work, we propose a single-frequency time-step method for ionospheric delay gradient estimation. Careful baseline length selection is needed, due to ionospheric piercing point movements. We applied our method to GNSS data in 2014, at the peak of the 24th solar cycle, and showed that the standard deviations of the vertical ionospheric delay gradients were comparable to those derived from the dual-frequency time-step method. The standard deviations of vertical ionospheric gradients, ranged between 4 and 6 mm/km. The {VIG}}values around the equinoxes were 1.5 mm/km greater than at other times.
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Instrumental Receiver Bias Estimation for Ionospheric Total Electron Content by Neural Network Model
(2023-10-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Budtho, Jirapoom
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Saekow, Apitep
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Sopon, Thanomsak
Total Electron Content (TEC) is one of the most important parameters in the study of the ionosphere, especially for determining ionospheric disturbances. The TEC levels are typically estimated from dual-frequency GPS observation data. Since the measured TEC contains discrepancies such as satellite and receiver biases, they need to be removed to obtain more accurate TEC values. In this work, we estimate the receiver bias using a neural network technique. Based on the exhaustive evaluation, we design a neural network (NN) model with two-hidden layers, and it is trained with datasets from three GNSS observation stations in Thailand. The prediction from the proposed neural network deviates from the baseline reference using the minimum standard deviation method with significantly faster computational time. The trained NN model is also tested for estimating the receiver bias values at other untrained stations in Thailand.
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Effects of Equatorial Plasma Bubbles over Real-Time Kinematic Positioning in Low-Latitude Region
(2023-01-01)
Thu, Phyo C.
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Supnithi, Pornchai
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Myint, Lin Min Min
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Budtho, Jirapoom
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Saito, Susumu
Equatorial plasma bubbles (EPBs) refer to ionospheric irregularities in low-latitude regions, commonly observed after sunset. They originate at the magnetic equator and then potentially spread to mid-latitude region. As cm-level positioning techniques are increasingly important to various segments of society, the performance degradation of these systems due to EPB at low latitudes needs to be investigated. In this work, we analyze the EPB effects on the performances of real-time kinematic (RTK) positioning at the short, medium, and long baselines at low-latitude stations in Thailand. The low-latitudes local ionospheric disturbances such EPBs are shown to degrade the positioning accuracy of RTK in different seasons in 2022. It is found that the positioning errors are higher during the disturbance periods and more severe at the long baselines than the shorter ones, especially during the equinoctial months.
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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.
1
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Multipath Analysis at Low-Latitude GNSS Stations around Suvarnabhumi Airport, Thailand, for GBAS Standards
(2021-01-01)
Budtho, Jirapoom
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Supnithi, Pornchai
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Saito, Susumu
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Siansawasdi, Nattapong
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Saekow, Apitep
The characteristics of the local area positioning error sources are important for Ground-Based Augmentation System (GBAS) service planning. Accurate standard deviation models are required for prior simulation of the performance of the aircraft precision landing system. The multipath standard deviation of the pseudo-range errors model is used in GBAS for each satellite elevation angle. This standard model is generated by collecting the multipath conditions from airports. However, some airports have different characteristics of the multipath effects than the others, resulting in inaccurate error models when applied to the GBAS operations. Therefore, in this work, we study and analyze a 1-dimensional curve-fitted model for the multipath error models at three GNSS stations near the Suvarnabhumi International Airport, Thailand. The results show that in the case that the multipath errors are distributed equally at each azimuth angle, the RMSEs are reduced from 0.1 to 0.02 meters near the 90-degree elevation angle and less than 0.05 meters at other degrees. For the AER1 station, located on the airport runway, in which the multipath errors are not distributed equally at each azimuth, the maximum RMSE, is less than 0.08 meters when compared with 0.14 meters from the GBAS model.
1
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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.
2
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Clustering of Ionospheric Irregularities based on Spatiotemporal ROTI Keogram Images
(2024-01-01)
Mutasov, Gleb
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Min Myint, Lin Min
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Supnithi, Pornchai
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Budtho, Jirapoom
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Tongkasem, Napat
Ionospheric irregularities associated with Equatorial plasma bubbles (EPB) can significantly impact navigation and communication systems. Therefore, their occurrences need to be studied and predicted. To solve the prediction problem, it is necessary to identify types of spatiotemporal characteristics as reference points for the predictive model. This work employs unsupervised machine learning algorithms to identify types of ionospheric irregularities due to EPB using the rate of total electron content index (ROTI) keograms. Two machine learning methods: two models, the Gaussian mixture model (GMM), and k-means, are considered. Comparative analysis is performed, and the optimal number of clusters is estimated using one classical, k-means and one additional - repeatability score, introduced in this work metric. The optimal GMM model successfully classifies three types of irregularity patterns offering valuable insights for the development of an effective EPB prediction model and enhancing our understanding of ionospheric behavior.
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Budtho, Jirapoom

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