Development of an ANN Interpolation Scheme for Estimating Missing Radon Concentrations in Ohio

Akkala, Arjun

doi:10.2174/1875040001104010021

Cited by 8 publications

(17 citation statements)

References 34 publications

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“…Though SMNN has better performance in evaluating FB among all the interpolation techniques, considering the remaining evaluation parameters, it can be mentioned that SMNN did not perform better over other interpolation techniques. However, ANNs and KBNNs have performed better over conventional interpolation techniques using the split‐sample validation . The CANN model has shown better performance in evaluating five of the performance measures among all the interpolation techniques discussed.…”

Section: Resultsmentioning

confidence: 99%

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi

Bhatt

Adusumilli

et al. 2014

Env Prog and Sustain Energy

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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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Section: Resultsmentioning

confidence: 99%

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Gummadi

Bhatt

Adusumilli

et al. 2014

Env Prog and Sustain Energy

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“…Also, the existing neural network structure as discussed in [11] is not suitable for extrapolation due to their limited generalization capability. Also, the existing neural network structure as discussed in [11] is not suitable for extrapolation due to their limited generalization capability.…”

Section: Prior Artmentioning

confidence: 99%

“…In our recent work [11], commonly used multilayer perceptrons (MLP) were utilized for the first time to model and estimate missing radon concentrations and has been proven to be better than traditional interpolation techniques such as kriging, radial basis functions, etc. In our recent work [11], commonly used multilayer perceptrons (MLP) were utilized for the first time to model and estimate missing radon concentrations and has been proven to be better than traditional interpolation techniques such as kriging, radial basis functions, etc.…”

Section: Introductionmentioning

confidence: 99%

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

Akkala

Bhatt

Devabhaktuni

et al. 2012

Env Prog and Sustain Energy

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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

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“…The basic ANN is a 3 layer perceptron known as Multi-Layer Perceptron (MLP3) [1][2][3]. Here, the accuracy of the neural networks depends on the availability of the training data.…”

Section: Introductionmentioning

confidence: 99%

A robust correction model based neural network modeling framework for electromagnetic simulations and RF measurements

Adusumilli

Almalkawi

Devabhaktuni

2013

82nd ARFTG Microwave Measurement Conference

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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.

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Development of an ANN Interpolation Scheme for Estimating Missing Radon Concentrations in Ohio

Cited by 8 publications

References 34 publications

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study

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

A robust correction model based neural network modeling framework for electromagnetic simulations and RF measurements

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