Predictive Modeling of Earthquakes in Los Angeles With Machine Learning and Neural Networks
Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji
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.