Comparative Analysis of Machine Learning Models for Earthquake Prediction: A Case Study of Düzce, Türkiye

Osman Dikmen

doi:10.55524/ijirem.2024.11.5.10

Abstract

This paper explores the application of machine learning models, specifically XGBoost, Stacking Regressor, and Long Short-Term Memory (LSTM), for predicting earthquake magnitudes in Düzce, Turkey. The models were trained and tested on seismic data to predict moment magnitude (Mw). The performance of each model was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that the XGBoost model outperforms the other models with a higher R² value and lower error metrics, providing a more accurate prediction of seismic events.

Keywords

Earthquake Prediction, LSTM, Machine Learning, Seismic Data, Stacking Regressor, XGBoost.

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Cites this article as

O. Dikmen "Comparative Analysis of Machine Learning Models for Earthquake Prediction: A Case Study of Düzce, Türkiye", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-11, Issue-5, Page No-73-82, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.5.10