How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

Bachelor thesis (2022)

Authors

I. Hashmi Electrical Engineering, Mathematics and Computer Science

Contributors

E. Isufi (supervisor 1)
M. Sabbaqi (supervisor 1)
Maosheng Yang (supervisor 1)
D.M.J. Tax Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Irtaza Hashmi
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Published Date

23-01-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we will investigate how CNN mixed with LSTM methods compare to the individual ones in predicting earthquakes given 30 seconds of seismic data before an earthquake occurs, also known as precursor data. Preliminary results show that a CNN mixed with LSTM has the best training accuracy while an individual LSTM performs best on unseen data.

Files

Research_Paper.pdf
(.pdf | 10.6 Mb)