A Comparison of Earthquake Prediction using Machine Learning Algorithms

Predicting earthquakes is one of the most challenging scientific endeavors due to the complex and dynamic nature of Earth's crust and the multitude of factors influencing seismic activity. Earthquake prediction research remains an essential scientific frontier, offering hope for better mitigation strategies and reduced impact in the future. This study examines the prediction of earthquake magnitudes using machine learning models, namely Random Forest Regression, Decision Tree Regression, and Support Vector Regression. Data such as latitude, longitude and depth were used to test and train the models to forecast magnitude. The performance of each model was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). According to the results, the Support Vector Regression model outperforms the others.