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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F17%3A00000319" target="_blank" >RIV/00025798:_____/17:00000319 - isvavai.cz</a>
Alternative codes found
RIV/86652052:_____/17:N0000062
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0265931X16302387?via%3Dihub" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0265931X16302387?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jenvrad.2016.07.008" target="_blank" >10.1016/j.jenvrad.2016.07.008</a>

Result language
angličtina
Original language name
Bagged neural network model for prediction of the mean indoor radon concentration in the municipalities in Czech Republic
Original language description
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 (∼22percent 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 name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10505 - Geology

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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