Pegoretti S, Verdi L
Dipartimento di Fisica, Università degli Studi di Trento, via Sommarive 14, I-38050 Povo (TN), Italy.
Radiat Prot Dosimetry. 2009 Dec;137(3-4):324-8. doi: 10.1093/rpd/ncp254. Epub 2009 Nov 13.
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 global and local scale) using the following approaches for estimating the concentration of indoor radon at locations that were not sampled were compared: geostatistical model, based on ordinary kriging, and machine learning (ML) technique. In the first case, algorithms designed for the specific and fine treatment (by modelling the variographic structure) of the spatial component of the phenomenon were used, whereas in the second case a model that can also exploit information linked to other variables that characterise each single dwelling in which the measure was conducted was used. For locations having large errors, the ML approach provides better results, due to the information related to 'soil contact' and 'building material'.
拥有一个可靠的预测工具对于正确识别氡易发生区域是必要的，尤其是在感兴趣的变量为室内氡浓度的情况下。对建筑物特征进行适当的描述变得至关重要。在这项工作中，比较了使用以下方法在未采样地点估计室内氡浓度时（在全球和局部尺度上）获得的结果：基于普通克里金法的地质统计模型和机器学习（ML）技术。在第一种情况下，使用了为该现象的空间成分进行特定和精细处理（通过对变异函数结构进行建模）而设计的算法，而在第二种情况下，使用了一种还可以利用与表征进行测量的每个单独住宅的其他变量相关信息的模型。对于误差较大的地点，由于与“土壤接触”和“建筑材料”相关的信息，ML方法能提供更好的结果。
