Work

Cloud radon data for earthquake magnitude prediction using machine learning

Year: 2024

Type: article

Abstract: The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.

Source: IAES International Journal of Artificial Intelligence

DOI: 10.11591/ijai.v13.i4.pp4572-4582

Authors: Thomas Oka Pratama, Sunarno Sunarno, Agus Budhie Wijatna, Eko Haryono

Institution: Universitas Gadjah Mada

Language: English

Topic: Earthquake Detection and Analysis

Subfield: Geophysics

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: diamond

Funder: Universitas Gadjah Mada