Earthquake prediction based on time series decomposition and deep learning

Abstract

Earthquakes are extremely destructive natural disasters, and accurately forecasting earthquakes is of great sig- nificance in reducing losses caused by earthquakes. To improve the accuracy of earthquake forecasting, we propose a novel method that integrates an improved artificial rabbit optimization algorithm (IARO), variational mode decomposition (VMD), and deep learning, named IARO-VMD-DFGNet, for earthquake time series fore- casting. Facing the challenges caused by the need for manual setting of VMD parameters, we propose the IARO to optimize the parameters of VMD, thus avoiding errors caused by manual parameter setting. Additionally, we also propose a data-driven deep learning model, DFGNet, for forecasting decomposed data. The data used in this study are earthquake catalogs, which include five variables: timestamp, longitude, latitude, depth, and magni- tude. Each variable is independently decomposed and forecasted. The performance of the model is evaluated using four metrics: mean squared error, mean absolute error, relative standard error, and root mean square error. Experimental results from four different earthquake catalogs demonstrate that the proposed model outperforms several other popular time series forecasting models. It achieves average reductions of 24.6%, 18.5%, 15.2%, and 13.7% across the four evaluation metrics, demonstrating significant competitive advantages. Therefore, applying IARO-VMD-DFGNet to earthquake forecasting is of great significance in reducing the harm caused by earthquakes.

Keywords

Artificial rabbit optimization algorithm / Earthquake prediction / Time series / Variational mode decomposition

CRediT authorship contribution statement

Hao Luo: Writing - review & editing, Writing - original draft, Su- pervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. Zhongyi Liu: Writing - original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. Liang Wang: Writing - review & editing, Writing - original draft, Validation. Hongxing Chen: Writing - review & editing, Writing - original draft. Caiyun Xia: Writing - review & editing, Writing- original draft.

Declaration of competing interest

The author declares the following financial interests/personal re- lationships which may be considered as potential competing interests: Hongxing Chen is currently employed by Shandong Keyue Technology Co, Jinan, China. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This research was funded by the National Key R & D Program of China (No. 2024YFC3013903), the Liaoning Revitalization Talents Program Outstanding Young Talent (No. XLYC2403133), the National Science Foundation of China (No. 52427805), and the Liaoning Pro- vincial Department of Education's Science and Technology Research Project (No. LJKMZ20220450).

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