A comparative analysis of methodologies of daily metroplex ozone concentration prediction. (1st January 2013)

• Record Type: Journal Article
• Authors: Cudney, Elizabeth A.; Corns, Steven M.; DasNeogi, Protyusha
• Is Part Of: International journal of quality engineering and technology. Volume 3:Number 4(2013)
• Page Start: 332
• Page End: 347
• Publication Date: 2013-01-01
• Subjects: T-method -- prediction -- linear regression method -- CART -- ozone concentration -- R-squared values -- adjusted R-squared values; Engineering -- Quality control -- Periodicals; Systems engineering -- Periodicals; 620.0045
• Languages: English
• ISSNs: 1757-2177

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.