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Putranto, Christophorus Arga
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Earthquake epicenter prediction from the Java-Bali radon gas telemonitoring station using machine learning Putranto, Christophorus Arga; Sunarno, Sunarno; Faridah, Faridah; Pratama, Thomas Oka
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp39-45

Abstract

Predicting the location of earthquake epicenters is a critical aspect of earthquake forecasting, as it complements efforts to determine the time and magnitude of seismic events. This research addresses the challenge posed by the uncertainty in epicenter locations, particularly along the extensive plate faults of Indo-Australia and Eurasia. In these regions, effective earthquake prediction is compromised without accurate epicenter information, impeding mitigation strategies and complicating disaster impact estimation. The primary objective of this study is to devise an algorithm for forecasting earthquake epicenter locations by harnessing variations in radon gas concentrations on southern Java Island, Indonesia, as a predictive precursor. Using a supervised machine learning approach, this study integrates radon gas concentration data to predict the distance between a radon gas telemonitoring station and the impending earthquake epicenter. Three distinct machine learning algorithms were evaluated using data from six Java-Bali radon gas telemonitoring stations within an early warning system. The random forest algorithm emerged as the most effective, yielding an average root mean square error of 453.10 kilometers. The findings of this research significantly contribute to earthquake risk mitigation efforts. This work enhances our capability to anticipate seismic events, and more effective disaster preparedness and response strategies in earthquake-prone regions.
Co-Authors Faridah Faridah Pratama, Thomas Oka Sunarno Sunarno