Development of an ANN Interpolation Scheme for Estimating Missing Radon Concentrations in Ohio

摘要

Radon (Rn) is a chemically inert, naturally occurring radioactive gas. It is one of the main causes of lung cancersecond to smoking, and accounts for about 25,000 deaths every year in the US alone according to the National CancerInstitute. In order to initiate preventive measures to reduce the deaths caused by radon inhalation, it is helpful to have radonconcentration data for each locality, e.g. zip code. However, such data are not available for each and every zip code inOhio, owing to several reasons including inapproachability. In places where data is unavailable, radon concentrationsmust be estimated using interpolation techniques.This paper presents a new interpolation technique based on Artificial Neural Networks (ANNs) for modeling and predictingradon concentrations in Ohio, US. Several ANNs were first trained and then validated using available data. From theresulting models, the model with lowest validation error was identified. Model accuracies using the proposed approachwas found to be significantly better compared to conventional interpolation techniques such as Kriging and Radial BasisFunctions.