Advancing Earthquake Prediction: A Comprehensive Review of Data Science Techniques

This paper delves into the transformative potential of data science for earthquake prediction techniques. Through a thorough literature review, it explores methodologies spanning machine learning, deep learning, time series analysis, signal processing, Bayesian models, and data integration, assessing their relevance in earthquake forecasting. Key algorithms, including Random Forest, Support Vector Regression, Gradient Boosting Machine, General Linear Model, and Artificial Neural Networks, are used to interpret seismic data. Using simulation results, this study underscores the promise of these varied methods in earthquake prediction. The research concludes by pinpointing prospective areas of study and emphasizing the need for cross-disciplinary cooperation to surmount challenges in earthquake prediction, ultimately aiming to reduce the catastrophic effects of earthquakes on societies and infrastructure.