| Yazarlar (5) |
Nazlican Gengec Zorkun
|
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Makbule Hilal Mutevelli Oncul
|
|
Secil Karatay
|
|
Faruk Erken
|
|
Cafer Budak
|
| Özet |
| This study investigates the potential of Machine Learning techniques to detect ionospheric precursors of major earthquakes using Total Electron Content (TEC) data. A Random Forest classification algorithm is implemented to IONOLAB-TEC estimates from four significant earthquakes in Türkiye between 2020 and 2023, with magnitudes ranging from Mw 6.7 to 7.8. The algorithm distinguishes between four classes: geomagnetically quiet days, geomagnetically disturbed days, three days preceding earthquakes and earthquake days. Nine statistical features are extracted from TEC data to characterize temporal and spatial ionospheric variations. Results demonstrate that the classifier effectively differentiates between seismo-ionospheric anomalies and other ionospheric disturbances with Macro-Average Accuracy ranging from 93.36% to 97.76%. A clear correlation between earthquake magnitude and classification ... |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Advances in Space Research |
| Dergi ISSN | 0273-1177 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 07-2025 |
| Doi Numarası | 10.1016/j.asr.2025.06.080 |
| Makale Linki | https://doi.org/10.1016/j.asr.2025.06.080 |
| Atıf Sayıları |
