A Comparative Analysis of Methodologies of Daily Metroplex Ozone Concentration Prediction

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. © 2013 Inderscience Enterprises Ltd.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Adjusted R-squared values; CART; Linear regression method; Ozone concentration; Prediction; R-squared values; T-method

International Standard Serial Number (ISSN)

1757-2185; 1757-2177

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Inderscience, All rights reserved.

Publication Date

01 Jan 2013

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