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An overview of forecast models evaluation for monitoring air quality management in the State of Texas, USA

An overview of forecast models evaluation for monitoring air quality management in the State of... Purpose – The purpose of this study is to investigate forecast models using data provided by the Texas Commission on Environmental Quality (TCEQ) to monitor and develop forecast models for air quality management. Design/methodology/approach – The models used in this research are the LDF (Fisher Linear Discriminant Function), QDF (Quadratic Discriminant Function), REGF (Regression Function), BPNN (Backprop Neural Network), and the RBFN (Radial Basis Function Network). The data used for model evaluations span a 12‐year period from 1990 to 2002. A control chart of the data is also examined for possible shifts in the distribution of ozone present in the Houston atmosphere during this time period. Findings – Results of this research reveal variables that are significantly related to the ozone problem in the Houston area. Practical implications – Models developed in this paper may assist air quality managers in modeling and forecasting ozone formations using meteorological variables. Originality/value – This is the first study that has extensively compared the efficiency of LDF, QDF, REGF, BPNN and RBFN forecast models used for tracking air quality. Prior studies have evaluated Neural Networks, ARIMA and regression models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Management of Environmental Quality: An International Journal Emerald Publishing

An overview of forecast models evaluation for monitoring air quality management in the State of Texas, USA

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Publisher
Emerald Publishing
Copyright
Copyright © 2009 Emerald Group Publishing Limited. All rights reserved.
ISSN
1477-7835
DOI
10.1108/14777830910922460
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this study is to investigate forecast models using data provided by the Texas Commission on Environmental Quality (TCEQ) to monitor and develop forecast models for air quality management. Design/methodology/approach – The models used in this research are the LDF (Fisher Linear Discriminant Function), QDF (Quadratic Discriminant Function), REGF (Regression Function), BPNN (Backprop Neural Network), and the RBFN (Radial Basis Function Network). The data used for model evaluations span a 12‐year period from 1990 to 2002. A control chart of the data is also examined for possible shifts in the distribution of ozone present in the Houston atmosphere during this time period. Findings – Results of this research reveal variables that are significantly related to the ozone problem in the Houston area. Practical implications – Models developed in this paper may assist air quality managers in modeling and forecasting ozone formations using meteorological variables. Originality/value – This is the first study that has extensively compared the efficiency of LDF, QDF, REGF, BPNN and RBFN forecast models used for tracking air quality. Prior studies have evaluated Neural Networks, ARIMA and regression models.

Journal

Management of Environmental Quality: An International JournalEmerald Publishing

Published: Jan 2, 2009

Keywords: Air pollution; Environmental management; Forecasting; Ozone; United States of America

References

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