A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones

Wella Wella, Ririn Ikana Desanti, Suryasari Suryasari
Pages 2421-2435
Abstract Views 1641
PDF Downloads 817
Crossref Citations -
DOI https://doi.org/10.47738/jads.v6i4.904

Abstract


Megathrust earthquakes are one of the most severe threats to countries situated along tectonic subduction zones, particularly Indonesia, where the movement of converging plates frequently triggers large-scale seismic events and tsunamis. Although recent developments in seismology have introduced various predictive tools, many of these models still face challenges, especially due to limitations in hydrogeological data quality. This study aims to investigate how three different machine learning algorithms perform in predicting megathrust earthquake events. The algorithms tested are Support Vector Machine, Random Forest, and Artificial Neural Network, applied to a dataset dominated by earthquake records from the Indonesian and Pacific regions. Each model was evaluated based on accuracy, precision, recall, and F1 score to provide a comprehensive performance analysis. The results show that Random Forest produced the highest accuracy, reaching 96%, followed closely by Support Vector Machine with 95%, while Artificial Neural Network achieved 83%. In terms of the F1 score, Random Forest led with a score of 0.95, indicating balanced performance in classification. However, recall, which is critical in disaster preparedness because it measures the model’s ability to detect high-risk events, Artificial Neural Network reached 92% for tsunami-related classifications. This suggests that while Random Forest is the most accurate overall, Artificial Neural Network could be more appropriate for early warning systems where the cost of missing a true event is much higher than issuing a false alarm. The contribution of this research is the direct comparison of multiple machine learning methods using real earthquake data, focusing not only on accuracy but also on practical disaster management considerations such as recall. This study also presents a novel perspective by analyzing the trade-off between model accuracy and disaster risk, emphasizing the need for probabilistic forecasts that can support timely public decision-making during seismic crises.

Keywords


Earthquake Prediction; Machine Learning; Random Forest; Neural Networks; Subduction Zones

Full Text:

PDF

References


S. Sufri and J. A. Lassa, “Integration of disaster risk reductionand climate change adaptation in Aceh: Progress and challenges after 20 Years of Indian Ocean Tsunamis,” International Journal of Disaster Risk Reduction, vol. 113, p. 104894, Oct. 2024, doi: 10.1016/j.ijdrr.2024.104894.

G. P. Carayannis, “The 26 December 2004 Earthquake in Indonesia-Future Earthquakes and Tsunamis in The Sumatra-Andaman Megathrust Region.,” Science of Tsunami Hazards, 2023, doi: http://dx.doi.org/10.1017/S1049023X00002880.

M. Munirwansyah, R. P. Munirwan, V. Listia, I. Irhami, and R. P. Jaya, “Sumatra-fault Earthquake Source Variation for Analysis of Liquefaction in Aceh, Northern Indonesia,” The Open Civil Engineering Journal, vol. 17, no. 1, Nov. 2023, doi: 10.2174/0118741495270939230921154841.

A. Chitkeshwar, “The Role of Machine Learning in Earthquake Seismology: A Review,” Archives of Computational Methods in Engineering, Mar. 2024, doi: 10.1007/s11831-024-10099-2.

G. Airlangga, “Analysis of Machine Learning Algorithms for Seismic Anomaly Detection in Indonesia: Unveiling Patterns in the Pacific Ring of Fire,” Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika, vol. 5, no. 1, pp. 37–48, Apr. 2024, doi: 10.46306/LB.V5I1.489.

J. Li et al., “Application of artificial intelligence technology in the study of anthropogenic earthquakes: a review,” Artif Intell Rev, vol. 58, no. 5, p. 155, Mar. 2025, doi: 10.1007/s10462-025-11157-2.

Z. Ying, W. Congcong, S. Didier, and Z. Chengxiang, “Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review,” Feb. 2025, Accessed: May 30, 2025. [Online]. Available: https://arxiv.org/pdf/2502.12161

O. N. Akarsu, “A Bibliometric Review of Earthquake and Machine Learning Research,” Civil Engineering Beyond Limits, vol. 5, no. 1, pp. 1–10, Apr. 2024, doi: 10.36937/cebel.2024.1908.

N. Alidadi and S. Pezeshk, “State of the art: Application of machine learning in ground motion modeling,” Eng Appl Artif Intell, vol. 149, Jun. 2025, doi: 10.1016/j.engappai.2025.110534.

M. S. Chowdhury, “Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting,” Environmental Challenges, vol. 14, Jan. 2024, doi: 10.1016/j.envc.2023.100800.

R. Jena et al., “Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia,” Geoscience Frontiers, vol. 11, no. 2, pp. 613–634, Mar. 2020, doi: 10.1016/j.gsf.2019.07.006.

L. Tang, M. Zhang, and L. Wen, “Support Vector Machine Classification of Seismic Events in the Tianshan Orogenic Belt,” J Geophys Res Solid Earth, vol. 125, no. 1, Jan. 2020, doi: 10.1029/2019JB018132.

N. S. M. Ridzwan and S. H. Md. Yusoff, “Machine learning for earthquake prediction: a review (2017–2021),” Earth Sci Inform, vol. 16, no. 2, pp. 1133–1149, Jun. 2023, doi: 10.1007/s12145-023-00991-z.

S. S. Shukla, J. Dhanya, P. Kumar, and V. Dutt, “An Ensemble Random Forest Model for Seismic Energy Forecast,” Oct. 24, 2024. doi: https://doi.org/10.5194/nhess-2024-129.

M. S. Alvarez-Alvarado et al., “Cyber-Physical Power Systems: A Comprehensive Review About Drivers, Standards, and Future Perspectives,” 2024. doi: 10.2139/ssrn.4687769.

O. M. Saad, A. G. Hafez, and M. S. Soliman, “Deep Learning Approach for Earthquake Parameters Classification in Earthquake Early Warning System,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 7, pp. 1293–1297, Jul. 2021, doi: 10.1109/LGRS.2020.2998580.

A. Mignan and M. Broccardo, “Neural Network Applications in Earthquake Prediction (1994–2019): Meta-Analytic and Statistical Insights on Their Limitations,” Seismological Research Letters, vol. 91, no. 4, pp. 2330–2342, Jul. 2020, doi: 10.1785/0220200021.

D. Blank and J. Morgan, “Can Deep Learning Predict Complete Ruptures in Numerical Megathrust Faults?,” Geophys Res Lett, vol. 48, no. 18, p. e2021GL092607, Sep. 2021, doi: 10.1029/2021GL092607;WGROUP:STRING:PUBLICATION.

B. Feng and G. C. Fox, “Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in Southern California,” in 2021 IEEE 17th International Conference on eScience (eScience), IEEE, Sep. 2021, pp. 99–107. doi: 10.1109/eScience51609.2021.00020.

S. Ommi and M. Hashemi, “Machine learning technique in the north zagros earthquake prediction,” Applied Computing and Geosciences, vol. 22, p. 100163, Jun. 2024, doi: 10.1016/j.acags.2024.100163.

T. Turino, R. E. Saputro, and G. Karyono, “Penerapan Model Ensemble Learning dengan Random Forest dan Multi-Layer Perceptron untuk Prediksi Gempa,” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 2, Feb. 2025, doi: 10.52436/1.jpti.667.

A. Pandit and S. Panda, “Prediction of earthquake magnitude using soft computing techniques: ANN and ANFIS,” Arabian Journal of Geosciences, vol. 14, no. 13, p. 1260, Jul. 2021, doi: 10.1007/s12517-021-07594-2.

M. A. Salam, L. Ibrahim, and D. S. Abdelminaam, “Earthquake Prediction using Hybrid Machine Learning Techniques,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 5, 2021, doi: 10.14569/IJACSA.2021.0120578.

I. Syarif, A. Prugel-Bennett, and G. Wills, “SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 14, no. 4, p. 1502, Dec. 2016, doi: 10.12928/telkomnika.v14i4.3956.

M. K. Suryadi, R. Herteno, S. W. Saputro, M. R. Faisal, and R. A. Nugroho, “Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 2, pp. 137–147, Mar. 2024, doi: 10.35882/jeeemi.v6i2.375.

A. K. Mamindla and Y. Ramadevi, “ANN-ABC Meta-Heuristic Hyper Parameter Tuning for Mammogram Classification,” J Theor Appl Inf Technol, vol. 101, no. 1, 2023.

L. Visengeriyeva, A. Kammer, I. Bär, A. Kniesz, and M. Plöd, “CRISP-ML(Q). The ML Lifecycle Process.” Accessed: Mar. 20, 2025. [Online]. Available: https://ml-ops.org/content/crisp-ml

R. V. Odug, “Earthquakes | Data Analytics.” Accessed: Apr. 07, 2025. [Online]. Available: https://www.kaggle.com/code/ravivarmaodugu/earthquakes-data-analytics/input

J. Pwavodi, A. U. Ibrahim, P. C. Pwavodi, F. Al-Turjman, and A. Mohand-Said, “The role of artificial intelligence and IoT in prediction of earthquakes: Review,” Artificial Intelligence in Geosciences, vol. 5, p. 100075, Dec. 2024, doi: 10.1016/j.aiig.2024.100075.

D. Baktibayev, B. Baigozha, I. Akhmetov, R. Mussabayev, A. Krassovitskiy, and A. Toleu, “Literature review on aftershock and earthquake prediction models aided by NLP summarization and ontology extraction techniques,” Procedia Comput Sci, vol. 238, pp. 579–586, 2024, doi: 10.1016/j.procs.2024.06.064.

S. M. Plescia and G. P. Hayes, “Geometric controls on megathrust earthquakes,” Geophys J Int, vol. 222, no. 2, pp. 1270–1282, Aug. 2020, doi: 10.1093/gji/ggaa254.

SMSTsunamiWarning, “Highest Magnitude & Biggest Earthquakes.” Accessed: Apr. 07, 2025. [Online]. Available: https://www.sms-tsunami-warning.com/pages/highest-magnitude-earthquake




DOI: https://doi.org/10.47738/jads.v6i4.904

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0