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Keywords:

  • artificial neural networks;
  • correction model;
  • indoor air quality measures;
  • interpolation;
  • Ohio;
  • radon

According to National Cancer Institute, radon is one of the major causes for lung cancer related deaths after smoking in US. To prevent deaths due to radon inhalation there is a need to determine the level of radon concentration in each locality, for example, zip code and this would help ease the identification of areas with high radon concentration thereby allowing the necessary preventive measures to be taken. However, factors like inapproachability hinder the process of estimating radon concentration in some places. In such places it is a common practice to estimate the radon concentrations using several interpolation techniques. In this article, a new approach that improves the accuracy of the neural model with the help of sensitivity-based correction model for modeling and estimating radon concentrations in Ohio is proposed. The results are compared with commonly used techniques such as kriging, radial basis function (RBF), inverse distance weighting (IDW), global polynomial interpolation (GPI), local polynomial interpolation (LPI), and the recently developed conventional ANN modeling approach. Further, model accuracies of all the above models are evaluated based on Willmott's Index and the ranked performance measures criteria with emphasis on the extreme-end (peak-end, low-end), and mid-range radon concentrations. The results demonstrate the effectiveness of the proposed approach in estimating the radon concentrations with the percentage improvement of 70–80% prediction accuracy, compared to the other techniques. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 1223–1233, 2013