Title: A comparative analysis of methodologies of daily metroplex ozone concentration prediction

Authors: Elizabeth A. Cudney; Steven M. Corns; Protyusha DasNeogi

Addresses: Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA ' Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA ' Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA

Abstract: This paper compares three methods of predicting the changes in ozone concentration: linear regression, classification and regression tree (CART) analysis, and the T-method. Using linear regression on these results, a linear equation defining the change of the independent variable versus the dependent variables is created. The strength of the relationship is assessed using the R-squared value and adjusted R-squared value. Classification and regression tree analysis uses a tree-building methodology to generate decision rules, using patterns from historical data obtained on both the dependent variable and the independent or 'predictor' variables to create a prediction model. The T-method is used to calculate an overall prediction based on the dynamic signal-to-noise ratio to obtain an overall estimate of the true value of the output for each signal member. It was found that for this nearly directly correlated dataset the T-method performed comparably to linear regression and was a better predictor than the CART method.

Keywords: T-method; prediction modelling; linear regression; classification; regression tree; CART; ozone concentration changes; adjusted R-squared values; dynamic SNR; signal-to-noise ratio.

DOI: 10.1504/IJQET.2013.055878

International Journal of Quality Engineering and Technology, 2013 Vol.3 No.4, pp.332 - 347

Received: 27 Dec 2012
Accepted: 08 Mar 2013
Published online: 19 Jul 2014 *

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