Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks

Authors

DOI:

https://doi.org/10.32890/jict2025.24.1.2

Keywords:

Deep learning, earthquake forecasting, LSTM, spatial distribution, seismology

Abstract

Located in the subduction zone of four tectonic plates, the high occurrence of seismic events is a severe threat in Indonesia. Mitigating the adverse effects of such disasters is essential to forecast the likelihood of future earthquakes. Consequently, developing a robust method of forecasting future earthquakes is critical to facilitate prevention and mitigation efforts. A reliable earthquake prediction method is necessary to reduce the after-effects to the greatest extent possible. This study utilises historical seismic and proposes innovative data pre-processing methods using K-means clustering to build a Long Short-Term Memory (LSTM) model for earthquake forecasting to overcome high-disparity locations. Four LSTM layers are embedded with adjusted fine-tuned network hyperparameters to enhance forecasting accuracy. The results attain 0.379816, 0.616292, and 0.414586 for Mean Square Error (MSE), Root MSE, and Mean Absolute Error, respectively, providing significant insights into earthquake prediction. In addition, predicted seismic occurrences are plotted on a map to display their geographic location within the specified research region. This research provides significant value in facilitating the efficient distribution of resources, such as evacuating residents impacted by earthquakes or reinforcing buildings and infrastructure, for emergency responders and policymakers.

Author Biographies

  • Zulkifli Tahir, Department of Informatics, Universitas Hasanuddin

    Dr. Zulkifli Tahir is an Assistant Professor in the Department of Informatics, Faculty of Engineering, Universitas Hasanuddin, Indonesia. His research interests lie in the areas of Web Technologies including progressive web apps, web server load balancing, and digital transformation for small and medium industries; Distributed System, Fog Computing and Internet of Things (IoT), with a focus on smart homes, solar panel monitoring, and resource allocation; and Machine Learning and Artificial Neural Networks for applications in maintenance decision support systems, industrial machine fault detection, and image processing.

  • Elly Warni, Department of Informatics, Universitas Hasanuddin

    Elly Warni is a senior lecturer in the Department of Informatics, Faculty of Engineering, Universitas Hasanuddin, Indonesia. Her research interests lie in the areas data mining and soft computing.

  • Muhammad Alief Fahdal Imran Oemar, Department of Informatics, Universitas Hasanuddin

    Muhammad Alief Fahdal Imran Oemar is an accomplished IT Developer and university lecturer at Hasanuddin University. His specialization is in Java and JavaScript programming languages. With a wealth of experience in software development, he excels in utilizing Java for backend systems and JavaScript for dynamic web solutions.

  • Muhammad Alwi Kayyum, Department of Informatics, Universitas Hasanuddin

    Muhammad Alwi Kayyum is a student and research assistant in the Informatics Department, Universitas Hasanuddin. He has experience in data analytics and software development.

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Published

28-01-2025

Issue

Vol. 24 No. 1 (2025): Journal of Information and Communication Technology (JICT) Vol.24, No.1, January 2025

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Articles

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How to Cite

Wardhani, T. P. M., Tahir, Z., Warni, E., Imran Oemar, M. A. F., & Kayyum, M. A. (2025). Deep Learning Approach in Seismology: Enhancing Earthquake Forecasting using K-Means Clustering and LSTM Networks . Journal of Information and Communication Technology, 24(1), 29-51. https://doi.org/10.32890/jict2025.24.1.2

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Harvested 2026-09-06
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Identifiers DOI 10.32890/jict2025.24.1.2 OpenAlex W4406891652 Scopus 85217473911