Prediction of GPS-TEC on Mw > 5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm
Yazarlar (2)
Doç. Dr. Seçil KARATAY Kastamonu Üniversitesi, Türkiye
Saide Eda Gül Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | IEEE Geoscience and Remote Sensing Letters (Q1) | ||
| Dergi ISSN | 1545-598X Dergi Bilgileri (2023) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2023 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2023 |
| Cilt / Sayı / Sayfa | 20 / 1 / 1–5 | DOI | 10.1109/LGRS.2023.3262028 |
| Makale Linki | http://dx.doi.org/10.1109/lgrs.2023.3262028 | ||
| UAK Araştırma Alanları |
İşaret İşleme
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| Özet |
| The detection of earthquake precursor signals a few days before the earthquake day is one of the most studied subjects today. In recent years, a strong correlation is observed between earthquakes and ionospheric parameters. In this study, a feed-forward back propagation artificial neural network (ANN) Bayesian regularization (BR) algorithm is applied to detect the seismic disturbances and anomalies by predicting global positioning system (GPS)-total electron content (TEC) on earthquake days with magnitude greater than 5. It is observed that TEC is predicted with greater error margins for the stations at a maximum distance of 50 km from the epicenters. The errors for earthquakes less than Mw 7 are smaller than those for greater than 7. |
| Anahtar Kelimeler |
| Artificial neural network (ANN) | Bayesian regularization backpropagation (BRB) | earthquake | ionosphere | total electron content (TEC) |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 16 |
| Scopus | 17 |
| Google Scholar | 20 |