Budtho, Jirapoom
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Preferred name
Budtho, Jirapoom
Alternative Name
Budtho, J.
Main Affiliation
Email
jirapoom.bu@kmitl.ac.th
20 results
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Item type:Publication, Effects of Equatorial Plasma Bubbles over Real-Time Kinematic Positioning in Low-Latitude Region(2023-01-01); ; ; Saito, SusumuEquatorial 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparative Analysis of Deep Learning Models for Daily Solar Indices Forecasting in Solar Cycle 25(2025-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Compatibility of Low-Cost GNSS Receivers for Total Electron Content (TEC) Analysis(2025-01-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identifying Geomagnetic Storms with Ionospheric Storm Scale for GNSS and Disaster Prevention(2020-03-01); ; ; ; Tangtrakunphaisan, UdomsitThis paper proposes an ionospheric storm scale (I-scale) for identifying the impact of geomagnetic or ionospheric storms in the Ionosphere for GNSS (global navigation satellite system) service and disaster prevention. The I-scale in this work is computed based on the observed foF2 at Chumphon station (10.72°N, 99.37°E) over equatorial latitude from January 2004 to July 2018. The results report that the severe geomagnetic storms, i.e., IP3 and IN3, seldom occur at Chumphon with the probabilities of 0.02% and 0.07%, respectively. The probability of quiet ionospheric condition is the maximum value of 70.73%. Meanwhile, the other I-scales sometimes occur and range from 0.60% to 13.97%. The benefits of the foF2-based I-scale are to indicate the violence level of geomagnetic storms and to announce the ionospheric irregularities in practice. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms(2025-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Nominal ionospheric delay gradient estimation at Suvarnabhumi airport, Thailand(2017-10-19); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multipath Analysis at Low-Latitude GNSS Stations around Suvarnabhumi Airport, Thailand, for GBAS Standards(2021-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Clustering of Ionospheric Irregularities based on Spatiotemporal ROTI Keogram Images(2024-01-01); ; Tongkasem, NapatIonospheric 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of Quiet Time Vertical Ionospheric Delay Gradients Around Suvarnabhumi Airport, Thailand(2018-09-01); ; Saito, S.Global Navigation Satellite System (GNSS) is vital to aircraft navigation at many phases of flight. To extend its use to the approach and landing phases, ground-based augmentation system is an important on-the-ground technology to reduce the positioning errors. However, nonuniform spatial ionospheric delays need to be assessed during ground-based augmentation system planning at each airport, particularly, in equatorial and low-latitude regions. In this work, we analyze the statistics of ionospheric delay gradients around Suvarnabhumi airport, Thailand. The ionospheric delay gradients are estimated using single-frequency code and carrier phase observation through the Kalman filter. To increase the success of the ratio test, the satellite elimination technique is then proposed. Based on the analysis between 2013 and 2016, we find that the background ionospheric delay gradients during equinox are higher than solstice, especially during September equinox 2013 when the gradients are about 9 mm/km. Moreover, the ionospheric delay gradients are more variable during daytime than nighttime.