Short-term Earthquake Prediction via Recurrent Neural Network Models

Comparison among vanilla RNN, LSTM and Bi-LSTM

Bachelor thesis (2022)

Authors

X. Du Electrical Engineering, Mathematics and Computer Science

Contributors

E. Isufi (supervisor 1)
Maosheng Yang (supervisor 1)
M. Sabbaqi (supervisor 1)
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 XIANGYU Du
Recurrent Neural Network Earthquake Prediction Long Short-term Memory Time Series Model Bidirectional Long Short-term Memory Over-fitting Grid Search
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Published Date

28-01-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is cardinal to detect strikes within a few minutes or hours in advance. In this paper, given thirty seconds of waveform signal before earthquakes happen, we compare the performances of three different recurrent neural networks, namely vanilla recurrent neural network (RNN), long short-term memory (LSTM) and bidirectional LSTM, on earthquake prediction. We choose recurrent neural networks because their inner structures take advantage of learning the temporal dependencies from time series sequence. Results show that LSTM has better performance predicting on unseen data than the other two networks.