AI-Driven Geospatial Analysis of Indoor Radon Levels: A Case Study in Chungcheongbuk-do, South Korea

Abstract

Radon is a naturally occurring radioactive gas found in many terrestrial materials, including rocks and soils. 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 such as lithology, soil depth texture, drainage, material composition, surface texture, soil thickness, calcium oxide and strontium levels, slope, topographic wetness index, wind exposure, valley depth, and the LS factor. These factors were analyzed using frequency ratios (FRs) to assess the influence on indoor radon distribution. The resulting maps were validated with several techniques, including FR, convolutional neural network, long short-term memory, and group method of data handling. The establishment of a geospatial database provided a basis for the integration and analysis of indoor radon levels, along with relevant geological, soil, topographical, and geochemical data. The study calculated the correlations between indoor radon and diverse factors statistically. The indoor radon potential was mapped for Chungcheongbuk-do by applying these techniques, to assess the potential radon distribution. The robustness of the validated model was assessed using the area under the receiver operating curve (AUROC) for both training and testing datasets.