Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic
Language English Country Great Britain, England Media print-electronic
Document type Journal Article
PubMed
27440462
DOI
10.1016/j.jenvrad.2016.07.008
PII: S0265-931X(16)30238-7
Knihovny.cz E-resources
- Keywords
- Bagged neural network, Czech Republic, Gamma dose rate, Indoor radon, Lithology, Predictive mapping,
- MeSH
- Radiation Monitoring * MeSH
- Neural Networks, Computer * MeSH
- Air Pollutants, Radioactive analysis MeSH
- Air Pollution, Radioactive statistics & numerical data MeSH
- Radon analysis MeSH
- Models, Theoretical MeSH
- Air Pollution, Indoor statistics & numerical data MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic MeSH
- Names of Substances
- Air Pollutants, Radioactive MeSH
- Radon MeSH
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.
Czech Geological Survey Geologicka 6 152 00 Praha 5 Czech Republic
National Radiation Protection Institute Bartoskova 28 140 00 Praha 4 Czech Republic
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