Information Technology: Faculty Publications
Predictive Modeling of Earthquakes in Los Angeles With Machine Learning and Neural Networks
Document Type
Conference Proceeding
Publication Date
8-5-2024
Publication Title
IEEE Access
DOI
10.1109/ACCESS.2024.3438556
Abstract
Earthquakes pose a significant threat to urban areas, necessitating accurate forecasting models to mitigate their impact. This study focuses on earthquake forecasting in Los Angeles, a region with high seismic activity and limited research. We established a feature matrix for forecasting earthquakes within a 30-day period by analyzing the most predictive patterns from recent studies. Our model developed a subset of features capable of forecasting the highest magnitude of an earthquake. Using advanced machine learning algorithms and neural networks, our model achieved an accuracy of 69.14% in forecasting the maximum magnitude earthquake as one of the 6 categories. We aim to provide a useful guideline for future researchers.
Recommended Citation
Emre Yavas, Cemil, Lei Chen, Christopher Kadlec, Yiming Ji.
2024.
"Predictive Modeling of Earthquakes in Los Angeles With Machine Learning and Neural Networks."
IEEE Access, 12: 108673-108702: Institute of Electrical and Electronics Engineers Inc..
doi: 10.1109/ACCESS.2024.3438556
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/193
Copyright
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Comments
Georgia Southern University faculty members, Lei Chen, Christopher Kadlec, and Yiming Ji co-authored "Predictive Modeling of Earthquakes in Los Angeles With Machine Learning and Neural Networks".