Exploring statistical and machine learning techniques to identify factors influencing indoor radon concentration

Dicu, T.; Cucoş, A.; Botoş, M.; Burghele, B.; Florică, Ş.; Baciu, C.; Ştefan, B.; Bălc, R.

doi:10.1016/j.scitotenv.2023.167024

Cited by 2 publications

(1 citation statement)

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“…These play roles in the movement of radon from the soil into indoor spaces and variation in the radon concentrations. Radon risk can be determined by amalgamating the radon potential associated with these soil properties; high correlations are associated with elevated radon levels (Cosma et al 2013a;Dicu et al 2023).…”

Section: Study Areamentioning

confidence: 99%

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

Widya,

Rezaie,

Lee

et al. 2024

Preprint

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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.

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Section: Study Areamentioning

confidence: 99%