Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms
| dc.contributor.author | Mutasov, Gleb | |
| dc.contributor.author | Supnithi, Pornchai | |
| dc.contributor.author | Budtho, Jirapoom | |
| dc.contributor.author | Tongkasem, Napat | |
| dc.contributor.author | Nishioka, Michi | |
| dc.contributor.author | Perwitasari, Septi | |
| dc.contributor.author | Myint, Lin Min Min | |
| dc.date.accessioned | 2026-08-06T10:49:25Z | |
| dc.date.available | 2026-08-06T10:49:25Z | |
| dc.date.issued | 2025-01-01 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 21769-21782, 2025 | |
| dc.identifier.doi | 10.1109/JSTARS.2025.3600477 | |
| dc.identifier.issn | 19391404 | |
| dc.identifier.other | 2-s2.0-105013674429 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/16512 | |
| dc.source | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | |
| dc.subject | Clustering | |
| dc.subject | equatorial ionospheric irregularities | |
| dc.subject | equatorial plasma bubbles | |
| dc.subject | ionospheric total electron content | |
| dc.subject | keograms | |
| dc.subject | unsupervised machine learning | |
| dc.title | Classification of Equatorial Ionospheric Irregularities Using Unsupervised Machine Learning Based on Spatiotemporal ROTI Keograms | |
| dc.type | Article |