Information Technology: Faculty Publications

Improving earthquake prediction accuracy in Los Angeles with machine learning

Document Type

Article

Publication Date

10-18-2024

Publication Title

Scientific Reports

DOI

10.1038/s41598-024-76483-x

Abstract

This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles.

Comments

Georgia Southern University faculty members, Lei Chen, Christopher Kadlec, and Yiming Ji co-authored "Improving earthquake prediction accuracy in Los Angeles with machine learning".

Copyright

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

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