Mapping Indoor Radon Concentrations in Chungcheongbuk-do, South Korea: A Geospatial Analysis using Machine Learning Models

Abstract Radon is a naturally occurring radioactive gas found in many terrestrial materials. Due to the potential health risks linked to persistent exposure to high radon concentrations, it is essential to investigate indoor radon accumulation. This study generated indoor radon index maps for Chungcheongbuk-do, South Korea, selected factors with frequency ratios (FRs) and validated them using the FR, convolutional neural network, long short-term memory, and group method of data handling machine learning models. The establishment of a geospatial database provided a basis for the integration and analysis of indoor radon concentrations (IRCs), along with relevant geological, soil, topographical, and geochemical data. The study calculated the correlations between IRC and diverse factors statistically. The IRC potential was mapped for Chungcheongbuk-do by applying the above techniques, to assess the potential radon distribution. The robustness of the validated model was assessed using the area under the receiver operating curve.