Exploring a new machine learning based probabilistic model for high-resolution indoor radon mapping, using the German indoor radon survey data

arXiv:2310.11143v2 Announce Type: replace-cross
Abstract: Radon is a carcinogenic, radioactive gas that can accumulate indoors. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sample often differ from the characteristics of the population due to the large number of relevant factors that control the indoor radon concentration such as the …

abstract arxiv cs.lg data effects german health knowledge machine machine learning mapping physics.data-an probabilistic model scale stat.ml survey survey data type