Attention-based bi-directional long-short term memory network for earthquake prediction

Andersson, K, Hossain, MS, Mahmud, M ORCID: https://orcid.org/0000-0002-2037-8348, Kaiser, MS, Taher, KA, Nahian, MJA, Ghosh, T and Banna, MHA, 2021. Attention-based bi-directional long-short term memory network for earthquake prediction. IEEE Access, 9, pp. 56589-56603. ISSN 2169-3536

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Abstract

An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. and Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.

Item Type:	Journal article
Publication Title:	IEEE Access
Creators:	Andersson, K., Hossain, M.S., Mahmud, M., Kaiser, M.S., Taher, K.A., Nahian, M.J.A., Ghosh, T. and Banna, M.H.A.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	6 April 2021
Volume:	9
ISSN:	2169-3536
Identifiers:	Number Type 10.1109/access.2021.3071400 DOI 1432454 Other
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:	Jonathan Gallacher
Date Added:	27 Apr 2021 15:11
Last Modified:	31 May 2021 15:03
URI:	https://irep.ntu.ac.uk/id/eprint/42763

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