Kanevski, MikhailMaignan, MichelPiller, GeorgesMinier, PhilippeSusini, Alberto2019-09-162019-09-162004https://dl.gi.de/handle/20.500.12116/27194The present work deals with development and adaptation of advanced geostatistical models and machine learning algorithms (statistical learning theory – Support Vector Machines) for comprehensive analysis and decision-oriented modelling of environmental spatial data. The real case study is based on indoor radon data. The inherent high variability at different spatial scales of noisy indoor radon measurements coupled with the heavy clustering effect of houses locations make this dataset an excellent candidate to assess the feasibility of traditional and advanced models, trend and risk mapping at local and regional scales.Text/Conference Paper