Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Banna, M.H.A., Taher, K.A., Kaiser, M.S., Mahmud, M. ORCID: 0000-0002-2037-8348, Rahman, M.S., Hosen, A.S.M.S. and Cho, G.H., 2020. Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges. IEEE Access.

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Abstract

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

Item Type: Journal article
Publication Title: IEEE Access
Creators: Banna, M.H.A., Taher, K.A., Kaiser, M.S., Mahmud, M., Rahman, M.S., Hosen, A.S.M.S. and Cho, G.H.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 9 October 2020
Identifiers:
NumberType
10.1109/access.2020.3029859DOI
1380871Other
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 28 Oct 2020 13:44
Last Modified: 28 Oct 2020 13:44
URI: http://irep.ntu.ac.uk/id/eprint/41423

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