Radon is a chemically inert, naturally occurring radioactive gas. It is one of the main causes of lung cancer second to smoking, and accounts for about 25,000 deaths every year in the US alone according to the National Cancer Institute. In order to initiate preventative measures to reduce the deaths caused by radon inhalation, it is helpful to have radon concentration data for each locality, e.g. zip code. However, such data are not available for every zip code in Ohio, owing to several reasons including inapproachability. In places where data is unavailable, radon concentrations must be estimated using interpolation techniques to take appropriate preventive measures against cancer.
This thesis proposes new interpolation techniques based on Artificial Neural Networks utilizing the available knowledge in terms of Radon concentration data and Uranium concentration data for modeling and predicting Radon concentrations in Ohio, US. Several models were first trained and then validated using available data to identify the best model for each technique. Model accuracies using the proposed approaches were proven to be significantly better in comparison to conventional interpolation techniques such as Kriging and Radial Basis Functions.