Identification of the ionospheric disturbances prior to large earthquakes in Türkiye using Random Forests algorithm
Yazarlar (5)
Arş. Gör. Nazlıcan GENGEÇ ZORKUN Kastamonu Üniversitesi, Türkiye
Arş. Gör. Makbule Hilal MÜTEVELLİ ÖNCÜL Kastamonu Üniversitesi, Türkiye
Doç. Dr. Seçil KARATAY Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Faruk ERKEN Kastamonu Üniversitesi, Türkiye
Doç. Dr. Cafer Budak Dicle Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Advances in Space Research (Q1) | ||
| Dergi ISSN | 0273-1177 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2026 |
| Cilt / Sayı / Sayfa | 0 / 1 / – | DOI | 10.1016/j.asr.2025.06.080 |
| Makale Linki | https://doi.org/10.1016/j.asr.2025.06.080 | ||
| 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 |
| Ionospheric precursors | Random Forest classification | Seismo-ionospheric coupling | Total Electron Content (TEC) | Türkiye earthquakes |
Science Direct
Identification of the ionospheric disturbances prior to large earthquakes in Türkiye using Random Forests algorithm
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |
| Google Scholar | 3 |