A comparative analysis of methodologies of daily metroplex ozone concentration prediction
by Elizabeth A. Cudney; Steven M. Corns; Protyusha DasNeogi
International Journal of Quality Engineering and Technology (IJQET), Vol. 3, No. 4, 2013

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.

Online publication date: Sat, 19-Jul-2014

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