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

Wella, Wella (2025) A Comparative Study of Machine Learning Approaches to Megathrust Earthquake Prediction in Subduction Zones. Journal of Applied Data Sciences, 6 (4). pp. 2421-2435. ISSN 27236471

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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.

Item Type:	Article
Creators:	Wella, Wella
Contributors:
Keywords:	Earthquake Prediction, Machine Learning, Random Forest, Neural Networks, Subduction Zones
Subjects:	000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining
Sustainable Development Goals:	Goal 11. Make cities and human settlements inclusive, safe, resilient and sustainable Goal 13. Take urgent action to combat climate change and its impacts Goal 09. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Divisions:	Faculty of Engineering & Informatics > Information System
Date Deposited:	02 Dec 2025 06:45
URI:	https://kc.umn.ac.id/id/eprint/42532

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