Earthquake Magnitude and Depth Prediction Based on Machine Learning and Multiple Linear Regression Models

This paper presents a comprehensive exploration of earthquake magnitude and depth prediction using an advanced machine learning model and multiple linear regression model. The study encompasses meticulous data preprocessing and feature engineering, extracting crucial attributes such as earthquake dates, epicenter coordinates, and depths. Leveraging a diverse set of models including LSTM (Long Short-Term Memory) neural network, multiple linear regression, support vector machines, gradient boosting trees, and neural networks, the research rigorously evaluates these models' performance through cross-validation. The results underscore the LSTM model's exceptional accuracy and stability in predicting earthquake magnitudes, while a hybrid stacked model integrating gradient boosting trees and neural networks demonstrates promising potential in-depth prediction.