Master of Science, University of Toledo, 2010, Engineering (Computer Science)

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.

Committee: Vijay Devabhaktuni PhD (Advisor); Ashok Kumar PhD (Advisor); Mohammed Niamat PhD (Committee Member) Subjects: Environmental Engineering

Master of Science in Engineering, University of Toledo, 2013, Engineering (Computer Science)

Radon-222 and its parent Radium-226 are naturally occurring radioactive decay products of Uranium-238. The US Environmental Protection Agency (USEPA) attributes about 10 percent of lung cancer cases that is `around 21,000 deaths per year' in the United States, caused due to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e. the average indoor radon screening level greater than 4 picocuries per liter). In order to implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This thesis presents a comparison between recently developed interpolation techniques to new techniques such as Support Vector Regression (SVR), and Random Forest Regression (RFR). Recently developed interpolation techniques include Artificial Neural Network (ANN), Knowledge Based Neural Networks (KBNN), Correction-Based Artificial Neural Networks (CBNN) and the conventional interpolation techniques such as Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Radial Basis Function (RBF) using the K-fold cross validation method.

Committee: William Acosta (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ashok Kumar (Committee Member); Rob Green (Committee Member) Subjects: Computer Science