AI-Driven Classification of Tsunami-Generating Earthquakes: Harnessing Random Forest, SVM, and Logistic Regression for Early Detection

Abstract

Predicting whether an earthquake will generate a tsunami is critical for early warning systems and disaster mitigation. In this study, we present an AI-driven approach to classify earthquakes as tsunami-generating or nontsunami events. We utilize three machine learning models—random forest, support vector machine, and logistic regression—trained on USGS earthquake data from 2015 to 2025, considering features, such as magnitude, depth, latitude, and longitude. Our exploratory data analysis highlights key correlations and feature distributions, while model evaluation demonstrates high classification performance, with random forest achieving up to 91% accuracy. We further investigate feature importance and provide ROC curves and confusion matrices for comparative analysis. The results show that AI-driven classification can effectively support early warning systems, offering a scalable and data-informed tool for seismic hazard assessment.