LSTM-based Models for Earthquake Prediction
Asmae Berhich, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj
Abstract
Over the last few years, many works have been done in earthquake prediction using different techniques and precursors in order to warn of earthquake damages and save human lives. Plenty of works have failed to sufficiently predict earthquakes, because of the complexity and the unpredictable nature of this task. Therefore, in this work we use the powerful deep learning technique. A useful algorithm that captures complex relationships in time series data. The technique is called long short-term memory (LSTM). The work employs this method in two cases of study; the first learns all the datasets in one model, the second case learns the correlations on two divided groups considering their range of magnitude. The results show that learning decomposed datasets gives more well-functioning predictions since it exploits the nature of each type of seismic events.