Earthquakes are abrupt and highly destructive phenomena, presenting a persistent challenge for accurate forecasting. The unpredictable and intricate behavior of seismic events often limits the effectiveness of conventional geological prediction techniques. This paper proposes a data-driven approach for earthquake prediction using supervised machine learning algorithms trained on historical seismic datasets. The study focuses on binary classification to determine whether an earthquake is significant, defined as having a magnitude of 6.0 or higher. Data preprocessing steps included handling missing values, encoding categorical features, and normalizing inputs. Two models—Random Forest and Support Vector Machine (SVM)—were implemented and compared based on their ability to classify seismic events. The Random Forest model achieved a higher accuracy of 88.84%, along with better recall and F1-scores in identifying significant earthquakes. Evaluation metrics such as the confusion matrix, ROC-AUC score, and feature importance analysis affirmed the effectiveness of the proposed models. The findings demonstrate that machine learning techniques can play a vital role in enhancing early warning systems and seismic risk assessment by improving the prediction of earthquake severity. This approach has the potential to support decision-making for disaster preparedness and emergency response planning. Future enhancements could include integrating real-time geospatial data and applying deep learning architectures to further improve model performance.
Introduction
Earthquakes are sudden, destructive natural disasters that are difficult to predict using traditional seismic analysis methods, which mainly identify risk zones but lack precision in timing and magnitude forecasts. The rise of machine learning (ML) offers new potential by analyzing historical seismic data to uncover complex patterns and improve prediction accuracy.
This study applies supervised ML techniques—specifically Random Forest and Support Vector Machine (SVM) classifiers—to predict whether earthquakes will be significant (magnitude ≥ 6.0) using a global earthquake dataset. The methodology includes data preprocessing, feature selection (magnitude, depth, latitude, longitude), model training, and evaluation with metrics like accuracy, precision, recall, F1-score, and ROC-AUC.
Results show that the Random Forest model significantly outperforms SVM, achieving higher accuracy (88.41%) and AUC (0.96). Depth and magnitude over time are key predictive features. These findings support the use of ML, particularly ensemble models, to enhance early warning systems and disaster preparedness. Future work aims to integrate real-time data and advanced deep learning to further improve earthquake forecasting.
Conclusion
This study presents a data-driven approach for predicting significant earthquakes using machine learning techniques. By analyzing historical seismic data with key features such as magnitude, depth, and geographic location, the research successfully implemented and evaluated two classification models—Random Forest and Support Vector Machine (SVM).The Random Forest classifier outperformed SVM across all major evaluation metrics, achieving a high accuracy of 88.84% and an AUC of 0.96. This improved performance is due to Random Forest’s ensemble design and its capacity to manage intricate and non-linear data relationships effectively.In contrast, the SVM model, while relatively simpler, was less effective, with an accuracy of 73.66% and a recall of only 0.45 for significant events, which limits its utility for high-risk applications.
The results demonstrate that machine learning, particularly ensemble learning, holds substantial promise for enhancing earthquake prediction systems. The successful detection of high-magnitude earthquakes (?6.0) supports the potential of these models to assist in disaster preparedness, resource allocation, and real-time risk monitoring. While the current system is based on historical data and focuses on binary classification, it lays a strong foundation for future enhancements. Incorporating real-time seismic sensor data, geospatial mapping, and deep learning architectures could further increase prediction accuracy and lead to the development of robust early warning systems.
In conclusion, this research highlights the effectiveness of machine learning in identifying seismic risks and opens up avenues for building intelligent, automated systems that can support proactive decision-making in natural disaster management.
References
[1] Song, Q., Wu, X., & Lv, Y. (2024). Evaluation of Earthquake Hazard Risk Level Based on Random Forest. International Journal of Computer Science and Information Technology, 2(2), 268–276.
[2] Jena, R., Pradhan, B., Al-Amri, A., Lee, C. W., & Park, H.-J. (2020). Earthquake probability assessment using deep learning algorithms in seismic zones of India. Sensors, 20(16), 4369.
[3] Kavianpour, P., Kavianpour, M., Jahani, E., & Ramezani, A. (2021). A CNN–BiLSTM model with attention mechanism for earthquake prediction. The Journal of Supercomputing.
[4] Utku, A., & Akcayol, M. A. (2024). A hybrid deep learning model for earthquake time prediction using CNN and GRU. Gazi University Journal of Science, 1172–1188.
[5] Wang, Y., Cao, Z., Lan, J., & Wang, Z. (2019). Deep learning for earthquake early warning: EEWNet. arXiv preprint arXiv:1912.05531.
[6] Xie, Y. (2024). Deep learning in earthquake engineering: A comprehensive review. arXiv preprint arXiv:2405.09021.
[7] Rouet-Leduc, B., Hulbert, C., & Johnson, P. A. (2017). Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 44(18), 9276–9282.
[8] DeVries, P. M. R., Viégas, F. B., Wattenberg, M., & Meade, B. J. (2018). Deep learning of aftershock patterns following large earthquakes. Nature, 560(7720), 632–634.
[9] Jozinovi?, D., Suppasri, A., & Imamura, F. (2020). Real-time ground motion prediction using convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing.
[10] Mousavi, S. M., & Beroza, G. C. (2019). Earthquake magnitude estimation using a deep neural network. Geophysical Research Letters, 46(4), 2095–2103.
[11] Berhich, N., Elhassouny, A., & El Hallaoui, A. (2023). An attention-based LSTM model for the prediction of strong earthquakes. Soil Dynamics and Earthquake Engineering.
[12] Sadhukhan, S., Khatua, K., & Mitra, S. (2023). Hybrid climatic and seismic data-driven model for earthquake prediction. Frontiers in Earth Science, 11, 1123983.
[13] Mignan, A., & Broccardo, M. (2020). Neural network applications in earthquake prediction: A meta-analysis. Seismological Research Letters, 91(4), 1956–1974.
[14] Zhu, L. (2020). A deep convolutional neural network for seismic phase picking. Physics of the Earth and Planetary Interiors, 300, 106430.
[15] Liu, Y., Zhu, W., & Beroza, G. C. (2020). Deep learning detection and location of earthquakes during the Ridgecrest sequence. Geophysical Research Letters, 47(4), e2019GL085576.
[16] Ji, Y., Zhang, X., & Zhao, Z. (2024). Predicting maximum earthquake magnitude using Random Forest classification. Scientific Reports, 14(1), 3882.
[17] Adi, S. P., Adishesha, V. B., Bharadwaj, K. V., & Narayan, A. (2020). Structural damage prediction using Random Forest and Gradient Boosting classifiers. American Journal of Biological and Environmental Statistics, 6(3), 55–61.
[18] Kong, L., Zhang, J., & Wu, Y. (2023). A scientometric analysis of machine learning in earthquake engineering. Applied Sciences, 13(4), 1745.
[19] Rouet-Leduc, B., Hulbert, C., Barros, K., et al. (2021). Predicting labquakes: A machine learning competition summary. Proceedings of the National Academy of Sciences (PNAS), 118(16), e2023297118.
[20] Wired Magazine. (2013). Why predicting earthquakes is so difficult—even for AI. Wired Science.