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Interpolation techniques for modeling and estimating indoor radon concentrations in Ohio: Comparative study
Author(s) -
Gummadi Jayaram,
Bhatt Deepak,
Adusumilli Srujana,
Devabhaktuni Vijay,
Acosta William,
Kumar Ashok
Publication year - 2015
Publication title -
environmental progress and sustainable energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.495
H-Index - 66
eISSN - 1944-7450
pISSN - 1944-7442
DOI - 10.1002/ep.11937
Subject(s) - radon , environmental science , interpolation (computer graphics) , support vector machine , statistics , computer science , mathematics , machine learning , artificial intelligence , motion (physics) , physics , quantum mechanics
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