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Improving earthquake prediction accuracy in Los Angeles with machine learning.

Cemil Emre Yavas1, Lei Chen2, Christopher Kadlec2

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This study uses machine learning to predict earthquake magnitudes in Los Angeles, California. The Random Forest model accurately forecasts maximum earthquake categories within 30 days, improving seismic risk assessment.

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Area of Science:

  • Geophysics and Seismology
  • Artificial Intelligence and Machine Learning

Background:

  • Accurate earthquake prediction remains a significant challenge for seismic risk management.
  • Los Angeles, California, faces substantial seismic hazards, necessitating improved prediction capabilities.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting maximum earthquake magnitude and category in Los Angeles.
  • To identify the most effective machine learning algorithm for short-term earthquake prediction.

Main Methods:

  • Construction of a comprehensive feature matrix integrating existing and novel predictive factors.
  • Evaluation of sixteen machine learning algorithms, including neural networks.
  • Application of the Random Forest model with a curated feature set for prediction.

Main Results:

  • The Random Forest model, utilizing a specific feature set, demonstrated high accuracy in predicting maximum earthquake category within a 30-day timeframe.
  • Random Forest outperformed the other fifteen evaluated machine learning algorithms.

Conclusions:

  • Machine learning, particularly the Random Forest algorithm, offers a powerful approach to enhance earthquake prediction accuracy.
  • These advancements hold significant potential for improving seismic risk management and preparedness in earthquake-prone regions like Los Angeles.