文章基本信息

标题：Development of an ANN Interpolation Scheme for Estimating Missing Radon Concentrations in Ohio
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作者：Arjun Akkala ; Vijay Devabhaktuni ; Ashok Kumar 等
期刊名称：The Open Environmental & Biological Monitoring Journal
电子版ISSN：1875-0400
出版年度：2011
卷号：4
页码：21-30
DOI：10.2174/1875040001104010021
出版社：Bentham Science Publishers Ltd
摘要：
Radon (Rn) 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 preventive 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 each and 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.

This paper presents a new interpolation technique based on Artificial Neural Networks (ANNs) for modeling and predicting radon concentrations in Ohio, US. Several ANNs were first trained and then validated using available data. From the resulting models, the model with lowest validation error was identified. Model accuracies using the proposed approach was found to be significantly better compared to conventional interpolation techniques such as Kriging and Radial Basis Functions.