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Prediction of GPS-TEC on Mw>5 Earthquake Days Using Bayesian Regularization Backpropagation Algorithm

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2023-01-01

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Metrikler

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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.

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Artificial neural networks | Artificial Neural Networks | Backpropagation | Bayes methods | Earthquake | Earthquakes | Ionosphere | Neurons | Precursor | Prediction algorithms | Total Electron Content | Training

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