Machine learning for the analysis of indoor radon distribution, compared with ordinary kriging

Having a reliable forecasting tool is necessary to correctly identify radon prone areas, especially in cases where the variable of interest is the indoor radon concentration. An appropriate characterisation of the features of the buildings becomes fundamental. In this work, the results obtained (in...

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Veröffentlicht in:	Radiation protection dosimetry 2009-12, Vol.137 (3-4), p.324-328
Hauptverfasser:	Pegoretti, S., Verdi, L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Air Pollution - analysis Air Pollution - statistics & numerical data Air Pollution, Indoor - analysis Air Pollution, Indoor - statistics & numerical data Artificial Intelligence Italy Radiation Dosage Radiation Monitoring - statistics & numerical data Radon - analysis
Online-Zugang:	Volltext
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