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Radon‐222 and its parent, radium‐226, is a naturally occurring radioactive decay product of uranium‐238. The US Environmental Protection Agency (USEPA) states that 10% of total lung cancer cases in the United States are directly attributable to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e., the average indoor radon screening level is greater than 4 pCi/L). 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 article presents a comparison between recently developed interpolation techniques such as neural network techniques, support vector regression (SVR), random forest regression (RFR), and the conventional interpolation techniques. The performance of all the techniques has been assessed using 10‐fold cross‐validation data. This study confirmed that the correction‐based artificial neural networks have performed better over SVR and RFR with least validation error of 3.63. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 169–177, 2015

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This paper introduces a new artificial neural networks (ANNs)-based correction-modeling approach for simulations and measurements. The proposed approach improves the accuracy of conventional neural models by reversing input-output variables in a systematic manner, while keeping the model structures simple relative to complex knowledge-based ANNs (KBNNs). The approach facilitates accurate/fast neural network modeling of practical electromagnetic (EM) structures, for which, training data is expensive. Two examples are presented to demonstrate the accuracy, efficiency, and feasibility of the proposed modeling approach. The first example is a broadband wire monopole antenna loaded by an annular dielectric ring resonator (DRR) at the antenna feed point. The second example is a metallic waveguide (WG) tube coated with inhomogeneous lossy materials for enhanced electromagnetic interference (EMI) shielding. The proposed approach is significant to RF circuit designers since it helps in building accurate models using reduced numbers of fullwave EM simulations and/or RF measurements.Index Terms -Artificial neural networks (ANNs), correction based neural network (CBNN), dielectric ring resonator (DRR), waveguide.