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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652052%3A_____%2F17%3AN0000062" target="_blank" >RIV/86652052:_____/17:N0000062 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00025798:_____/17:00000319

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0265931X16302387" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0265931X16302387</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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Environmental Radioactivity

ISSN

0265-931X

e-ISSN

—

Svazek periodika

166

Číslo periodika v rámci svazku

SI - Part 2

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

5

Strana od-do

398-402

Kód UT WoS článku

000390073700018

EID výsledku v databázi Scopus

2-s2.0-84992323861