Citation

Waworundeng, Jacquline and Tan, Wooi Haw and Lo, Yew Chiong and Ooi, Chee Pun (2025) Enhancing Earthquake Magnitude Prediction through Machine Learning with Indonesian Seismic Data. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

Earthquake prediction is crucial for mitigating seismic hazards and enhancing public safety. Traditional prediction methods often face challenges due to the complex and nonlinear nature of seismic data. This paper addresses the gap by employing machine learning techniques to forecast earthquake magnitudes, utilizing a comprehensive dataset from the Indonesian Agency for Meteorology, Climatology and Geophysics (Badan Meteorologi, Klimatologi, dan Geofisika/BMKG) and the United States Geological Survey (USGS) covering the period from 2008 to 2025. The primary objective is to assess the efficacy of various regression models in predicting earthquake magnitudes. The methodology involves preprocessing the seismic dataset to extract relevant features, followed by training multiple regression models such as linear regression, K-Nearest Neighbors, and various ensemble learning methods. These models are evaluated in terms of mean absolute error (MAE). The Light Gradient Boosting Machine (LightGBM) model outperformed other models by demonstrating its superior capability in capturing the intricate patterns inherent in seismic data. This study demonstrates the feasibility of integrating advanced machine learning models into seismic monitoring systems to improve the accuracy of earthquake forecasts for better disaster preparedness.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Earthquake
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3001-3521 Distribution or transmission of electric power
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 07:50
Last Modified:	18 Mar 2026 07:50
URII:	http://shdl.mmu.edu.my/id/eprint/15562

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