Scopus: Prediction of GPS-TEC on Mw>5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm
| dc.contributor.author | Karatay S. | |
| dc.contributor.author | Gul S.E. | |
| dc.date.accessioned | 2023-04-11T22:07:39Z | |
| dc.date.accessioned | 2023-04-12T00:30:52Z | |
| dc.date.available | 2023-04-11T22:07:39Z | |
| dc.date.available | 2023-04-12T00:30:52Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | 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 Backpropagation Artificial Neural Network Bayesian Regularization algorithm is applied to detect the seismic disturbances and anomalies by predicting GPS-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 <italic>Mw</italic> 7 are smaller than those for greater than 7. | |
| dc.identifier.doi | 10.1109/LGRS.2023.3262028 | |
| dc.identifier.issn | 1545598X | |
| dc.identifier.scopus | 2-s2.0-85151537398 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12597/4200 | |
| dc.relation.ispartof | IEEE Geoscience and Remote Sensing Letters | |
| dc.rights | false | |
| dc.subject | Artificial neural networks | Artificial Neural Networks | Backpropagation | Bayes methods | Earthquake | Earthquakes | Ionosphere | Neurons | Precursor | Prediction algorithms | Total Electron Content | Training | |
| dc.title | Prediction of GPS-TEC on Mw&#x003E;5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm | |
| dc.type | Article | |
| dspace.entity.type | Scopus | |
| person.affiliation.name | Kastamonu University | |
| person.affiliation.name | Kastamonu University | |
| person.identifier.scopus-author-id | 35102434600 | |
| person.identifier.scopus-author-id | 58169292200 | |
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