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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Rony Wijaya
Amakusa Instrumentation Technology
Computer Science & IT

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Earthquake magnitude prediction based on radon cloud data near Grindulu fault, Indonesia using the statistical method Sunarno Sunarno; Thomas Oka Pratama; Faridah Faridah; Nugroho Ananto; Hermin Kartika Sari; Rony Wijaya; Memory Motivanisman Waruwu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26494

Abstract

Earthquake prediction is one of the most challenging and vital tasks that demands new methodologies for improving the accuracy of predictions. The research aims to present how radon gas concentration fluctuations are associated with the prediction of earthquakes in the Eurasian-Indo-Australian Plates. The paper discusses a statistical method of forecasting earthquake magnitudes greater than M4.5 from real-time radon gas monitoring close to the Grindulu Fault, Pacitan, East Java, Indonesia. This developed model has had the least errors in the form of mean absolute error (MAE), 0.30; mean absolute percentage error (MAPE), 0.06; root mean square error (RMSE), 0.55; mean squared error (MSE), 0.30; symmetric mean absolute percentage error (SMAPE), 0.06; complex normalized mean absolute percentage error (cnMAPE), 0.97; error absolute average (EAA), 0.30; and error relative average (ERA), -0.11, showing great accuracy and uniformity in prediction. These observations support the model’s efficiency that may be adopted in earthquake early warning systems for better disaster preparedness. Predictive errors are reduced, and there is support for improved disaster management strategy, public safety education, and effective emergency response personnel training. This study can be used as a foothold for further advances in earthquake prediction methodologies and refinement of early warning systems.
Co-Authors Faridah Faridah Hermin Kartika Sari Memory Motivanisman Waruwu Nugroho Ananto Sunarno Sunarno Thomas Oka Pratama