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Knowledge-based neural network approaches for modeling and estimating radon concentrations

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Radon is a chemically inert, tasteless, and odorless gas, which causes lung cancer in people who are exposed to higher concentrations for extended periods of time. It is a byproduct of the decay of uranium in the soil. High concentrations are present in closed units, like houses, schools, etc. To identify houses with unacceptably high radon levels, the radon concentration for each zip code in Ohio needs to be measured. However, not all of the zip codes are surveyed, owing to reasons such as inapproachability. In places where data is unavailable, the concentrations need to be estimated using interpolation techniques. This article proposes the application of knowledge-based neural network approaches, namely prior knowledge input method, source difference method, and space-mapped neural network method for modeling and predicting radon concentrations. To this end, knowledge available in the form of uranium concentration data is exploited. Modeling accuracies of the proposed techniques are compared with those of the commonly used multilayer perceptron networks (employed in one of our recent work) and conventional interpolation techniques. © 2012 American Institute of Chemical Engineers Environ Prog, 32: 355-364, 2013