Earthquake Prediction Model Using Random Forest and Gradient Boosting Algorithms

Accurate earthquake prediction remains a significant challenge in geosciences. This research investigates the application of machine learning algorithms, specifically Random Forest and Gradient Boosting Algorithms, for earthquake prediction. By analyzing historical seismic data, these models aim to identify patterns and predict the likelihood of future earthquake occurrences. This study explores the potential of these machine learning techniques to improve earthquake prediction accuracy and contribute to better preparedness and mitigation strategies for this natural disaster.