Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics

This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems.