RETRACTED ARTICLE: Improving earthquake prediction accuracy in Los Angeles with machine learning

This research breaks new ground in earthquake prediction for LosAngeles, California, by leveraging advanced machine learning and neural networkmodels. We meticulously constructed a comprehensive feature matrix to maximizepredictive accuracy. By synthesizing existing research and integrating novelpredictive features, we developed a robust subset capable of estimating the maximumpotential earthquake magnitude. Our standout achievement is the creation of afeature set that, when applied with the Random Forest machine learning model,achieves a high accuracy in predicting the maximum earthquake category within thenext 30 days. Among sixteen evaluated machine learning algorithms, Random Forestproved to be the most effective. Our findings underscore the transformativepotential of machine learning and neural networks in enhancing earthquake predictionaccuracy, offering significant advancements in seismic risk management andpreparedness for Los Angeles.