Machine learning for earthquake prediction: a review (2017–2021)

For decades, earthquake prediction has been the focus of research using various methods and techniques. It is difficult to predict the size and location of the next earthquake after one has occurred. However, machine learning (ML)-based approaches and methods have shown promising results in earthqua...

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Bibliographic Details
Published in:Earth science informatics 2023-06, Vol.16 (2), p.1133-1149
Main Authors: Ridzwan, Nurafiqah Syahirah Md, Yusoff, Siti Harwani Md
Format: Article
Language:English
Subjects:
Algorithms
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Earthquake magnitude
Earthquake prediction
Earthquakes
Indicators
Information Systems Applications (incl.Internet)
Machine learning
Ontology
Review
Seismic activity
Seismic response
Simulation and Modeling
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
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Summary:For decades, earthquake prediction has been the focus of research using various methods and techniques. It is difficult to predict the size and location of the next earthquake after one has occurred. However, machine learning (ML)-based approaches and methods have shown promising results in earthquake prediction over the past few years. Thus, we compiled 31 studies on earthquake prediction using ML algorithms published from 2017 to 2021, with the aim of providing a comprehensive review of previous research. This study covered different geographical regions globally. Most of the models analysed in this study are keen on predicting the earthquake magnitude, trend and occurrence. A comparison of different types of seismic indicators and the performance of the algorithms were summarized to identify the best seismic indicators with a high-performance ML algorithm. Towards this end, we have discussed the highest performance of the ML algorithm for earthquake magnitude prediction and suggested a potential algorithm for future studies.
ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-023-00991-z