Ranking Earthquake Prediction Algorithms: A Comprehensive Review of Machine Learning and Deep Learning Methods

Abstract

Earthquake prediction is a complex and critical challenge within seismology, with significant implications for disaster mitigation and risk management. Recent advancements in Machine Learning and Deep Learning techniques have shown promising accuracy and reliability of earthquake prediction models. This review analyses various algorithms and methods for seismic event forecasting, focusing on Supervised, Unsupervised, and Deep Learning approaches. Additionally, other applications of these methods that are closely associated with the field of earthquake engineering (i.e., early warning systems) have also been extensively reviewed. A novel point-based ranking system is introduced, which simultaneously factors in the popularity of algorithms (measured by the number of published papers from 1951 to 2024) and their predictive accuracy. This system was applied to an analysis of 8644 research papers, enabling the identification of the top-performing algorithms in each category. Among the most effective methods are Regression (including linear, polynomial, and logistic regression), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) for supervised learning, Clustering (including K-Means and other types), and Principal Component Analysis (PCA) for unsupervised learning, and Artificial Neural Networks (ANNs), Long-Short Term Memory (LSTM), and Convolutional Neural Networks (CNNs) for Deep Learning. While these algorithms show significant potential, challenges such as data variability, feature selection, and model interpretability persist. The review emphasizes the need for continued development of more robust and scalable models and interdisciplinary collaboration to enhance earthquake prediction capabilities and improve early warning systems.