Tools
Tools
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.
|
Text
1380871_Mahmud.pdf - Published version Download (5MB) | Preview |
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: |
|
||||||
| 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: | 31 May 2021 15:14 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/41423 |
Actions (login required)
 |
Edit View |
Views
Views per month over past year
Downloads
Downloads per month over past year
