Earthquake Prediction Using Time Series with Deep Learning Models

An earthquake is a ground tremor that takes place when energy is abruptly discharged within the Earth's crust, sending seismic waves across the surface. In seismology, accurately predicting this phenomenon is of utmost importance, as precise predictions can save many lives. Numerous methods have been developed for earthquake prediction to date. However, since earthquakes generally exhibit a complex and unpredictable nature, achieving successful results can be quite challenging. In this study, it is proposed that using deep learning models and more comprehensive datasets could be beneficial in obtaining more effective results. This study aims to analyze seismic activity trends and forecast earthquake magnitudes using time series deep learning models. After reviewing various sources, Long Short-Term Memory (LSTM) has been identified as a suitable option for this study due to its memory retention capability. Additionally, to enhance the success rate, Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), and Recurrent Neural Network (RNN) models have also been incorporated into the study.