Intelligent Tsunami Forecasting System

This work proposes a machine learning–oriented framework for tsunami prediction that seeks to overcome the constraints of conventional numerical forecasting. Four algorithms—Decision Tree Classifier, Decision Tree Regressor, Linear Regression, and Random Forest Classifier—are compared using 22,797 tsunami records obtained from NOAA’s Global Historical Tsunami Database. Among these, the Random Forest Classifier performed best, achieving 95.92% accuracy and precision scores of 99%, 96%, and 95% for Safe, Moderate, and Severe classes, respectively. Unlike physics-based simulations that may require over 30 minutes to produce alerts, the system is capable of near real-time prediction, making it suitable for emergency decision-making. Synthetic data generation techniques are also applied to address the scarcity of historical tsunami events, resulting in a more robust early warning framework that can be integrated with international monitoring systems for disaster risk reduction.