| Yazarlar |
Doç. Dr. Seçil KARATAY
Kastamonu Üniversitesi, Türkiye |
Saide Eda Gül
|
| 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 |
| Earthquakes,Artificial neural networks,Training,Neurons,Bayes methods,Backpropagation,Prediction algorithms,Artificial neural network (ANN),Bayesian regularization backpropagation (BRB),earthquake,ionosphere,total electron content (TEC) |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | IEEE Geoscience and Remote Sensing Letters |
| Dergi ISSN | 1545-598X |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2023 |
| Cilt No | 20 |
| Sayfalar | 1 / 5 |
| Doi Numarası | 10.1109/LGRS.2023.3262028 |
| Makale Linki | http://dx.doi.org/10.1109/lgrs.2023.3262028 |
| Atıf Sayıları | |
| WoS | 2 |
| SCOPUS | 2 |
