Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic

The purpose of the study is to determine radon-prone areas in the Czech Republic based on the measurements of indoor radon concentration and independent predictors (rock type and permeability of the bedrock, gamma dose rate, GPS coordinates and the average age of family houses). The relationship between the mean observed indoor radon concentrations in monitored areas (∼22% municipalities) and the independent predictors was modelled using a bagged neural network. Levels of mean indoor radon concentration in the unmonitored areas were predicted using the bagged neural network model fitted for the monitored areas. The propensity to increased indoor radon was determined by estimated probability of exceeding the action level of 300Bq/m³. - Highlights: • Bagged neural network model used for prediction of the indoor radon concentration. • Specific characteristics of every municipality were taken into account for modelling. • The propensity for increased indoor radon was computed as an index per municipality. • We determined the radon-prone areas where indoor radon exceeded 300 Bqm⁻³ in at least 20% of family houses.