Deep learning-enhanced geospatial modeling for indoor radon mapping in Jeollabuk-do, South Korea

Abstract

Radon (Rn-222) is a naturally occurring radioactive gas that poses significant lung cancer risks when accumulated indoors, making accurate predictions of its spatial distribution crucial for public health. This study developed a high-resolution radon potential map for Jeollabuk-do, South Korea, using deep learning algorithms. A multivariate spatial database was compiled by integrating geological, geochemical, topographical, soil, and land-use variables. Fourteen input variables, including lithology, distance to faults, barium, potassium oxide, magnesium oxide, zinc, zirconium, wind exposition index, LS-factor (slope length and steepness), surface soil texture, deep soil texture, topography, effective soil thickness, and land use were used. Deep learning models, specifically Convolutional Neural Networks and Long Short-Term Memory networks, were implemented within a GIS framework to generate a predictive radon potential map by modeling relationships between the input variables and indoor radon concentrations, thereby identifying high-risk areas. The resulting radon potential map, produced at a 10 m spatial resolution, was validated using the receiver operating characteristic–area under the curve, achieving an accuracy of approximately 85%. The findings of this study provide a robust foundation for enhancing indoor air quality management and radiation protection strategies. © 2025 Elsevier B.V., All rights reserved.