<![CDATA[Tsunamis, triggered by seismic activities, pose significant threats to coastal regions, necessitating accurate prediction models to mitigate their impact. This study explores the application of machine learning models, including ensemble methods (Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost) and deep learning (Neural Networks), for tsunami prediction based on seismic data. The dataset spans seismic events from 1995 to 2023, characterized by features such as magnitude, depth, and geographic location. A 10-fold cross-validation approach was employed to evaluate model performance using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlight that Gradient Boosting achieved the best balance between precision and recall, with an F1-score of 0.6544 and the highest ROC-AUC of 0.8606, demonstrating its strong discriminatory power. Random Forest excelled in precision (0.6920) and F1-score (0.6287), making it suitable for reducing false positives. Ensemble boosting models, such as CatBoost and LightGBM, offered consistent performance with low variability across folds. In contrast, Neural Networks underperformed, achieving an F1-score of 0.5497 and an ROC-AUC of 0.7936, indicating the need for further optimization. Despite promising results, challenges in recall scores underscore the need for enhanced detection of tsunami-triggering events. The findings establish ensemble methods, particularly Gradient Boosting and Random Forest, as robust tools for tsunami prediction, providing a foundation for early warning systems. Future work will focus on improving recall and exploring hybrid modeling techniques to optimize predictive accuracy and reliability.]]>
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