On the use of VMD-LSTM neural network for approximate earthquake prediction

Qiyue Wang, Yekun Zhang, Jiaqi Zhang, Zekang Zhao and Xijun He ()
Additional contact information
Qiyue Wang: Beijing Technology and Business University (BTBU)
Yekun Zhang: Beijing Technology and Business University (BTBU)
Jiaqi Zhang: Beijing Technology and Business University (BTBU)
Zekang Zhao: Beijing Technology and Business University (BTBU)
Xijun He: Beijing Technology and Business University (BTBU)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 14, No 32, 13367 pages

Abstract: Abstract Earthquake prediction has been widely studied in many fields using various technologies, including machine learning, which is able to explore the underlying information of data. This study adopts machine learning for earthquake prediction, and employs the well-studied long short-term memory (LSTM) neural networks to predict the earthquake occurrence time, longitude, latitude, and magnitude. A variational mode decomposition (VMD) approach is also used to improve the precision of the precision. The proposed model is referred to as VMD-LSTM. The used datasets consist of earthquake catalogs from 1935 to 2023 in the Taiwan region, where earthquakes having magnitudes greater than 5.0 are studied. Four VMD-LSTMs are constructed to predict the earthquake occurrence time, longitude, latitude, and magnitude. The experimental results show that the proposed model has high performance on the test set. The results are also compared with those obtained by the LSTM model without VMD, verifying that the VMD-LSTM model leads to higher performance. This study introduces a novel model for the prediction of earthquake magnitude, location, and time. It also demonstrates that the VMD method can be combined with various neural networks to increase the prediction accuracy.

Keywords: Earthquake prediction; LSTM; VMD; Deep learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06724-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06724-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06724-9

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().