Karatay S.Gul S.E.2023-04-112023-04-122023-04-112023-04-122023-01-011545598Xhttps://hdl.handle.net/20.500.12597/4200Detection 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.falseArtificial neural networks | Artificial Neural Networks | Backpropagation | Bayes methods | Earthquake | Earthquakes | Ionosphere | Neurons | Precursor | Prediction algorithms | Total Electron Content | TrainingArticle10.1109/LGRS.2023.32620282-s2.0-85151537398