Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area

Chivalai Temiyasathit; Seoung Bum Kim; Neelesh V. Sule

Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area

Chivalai Temiyasathit, Seoung Bum Kim, Neelesh V. Sule

Research output: Contribution to conference › Paper › peer-review

Abstract

Vast amounts of data are being generated to extract implicit patterns of ambient air pollutants. This study attempts to investigate the behavior of daily maximum 8-hour ozone concentrations measured at 15 monitoring sites in the Dallas-Fort Worth area from June 1, 2002 to May 31, 2006. Time-series models were constructed to predict ozone concentrations. Regression trees were developed to study how meteorological variables impact ozone concentrations. The diagnostic test demonstrated the accurate predictability of the constructed time-series model. Further, regression trees identified important variables to predict ozone concentrations.

Original language	English
Pages	670-675
Number of pages	6
Publication status	Published - 2007
Externally published	Yes
Event	IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Nashville, TN, United States Duration: 2007 May 19 → 2007 May 23

Other

Other	IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World
Country/Territory	United States
City	Nashville, TN
Period	07/5/19 → 07/5/23

Keywords

Ozone prediction
Time series analysis
Tree-based model

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Cite this

Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area. / Temiyasathit, Chivalai; Kim, Seoung Bum; Sule, Neelesh V.
2007. 670-675 Paper presented at IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World, Nashville, TN, United States.

Research output: Contribution to conference › Paper › peer-review

@conference{ce8968ae8017408fb3b01a66247fdf36,

title = "Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area",

abstract = "Vast amounts of data are being generated to extract implicit patterns of ambient air pollutants. This study attempts to investigate the behavior of daily maximum 8-hour ozone concentrations measured at 15 monitoring sites in the Dallas-Fort Worth area from June 1, 2002 to May 31, 2006. Time-series models were constructed to predict ozone concentrations. Regression trees were developed to study how meteorological variables impact ozone concentrations. The diagnostic test demonstrated the accurate predictability of the constructed time-series model. Further, regression trees identified important variables to predict ozone concentrations.",

keywords = "Ozone prediction, Time series analysis, Tree-based model",

author = "Chivalai Temiyasathit and Kim, {Seoung Bum} and Sule, {Neelesh V.}",

year = "2007",

language = "English",

pages = "670--675",

note = "IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World ; Conference date: 19-05-2007 Through 23-05-2007",

}

TY - CONF

T1 - Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area

AU - Temiyasathit, Chivalai

AU - Kim, Seoung Bum

AU - Sule, Neelesh V.

PY - 2007

Y1 - 2007

N2 - Vast amounts of data are being generated to extract implicit patterns of ambient air pollutants. This study attempts to investigate the behavior of daily maximum 8-hour ozone concentrations measured at 15 monitoring sites in the Dallas-Fort Worth area from June 1, 2002 to May 31, 2006. Time-series models were constructed to predict ozone concentrations. Regression trees were developed to study how meteorological variables impact ozone concentrations. The diagnostic test demonstrated the accurate predictability of the constructed time-series model. Further, regression trees identified important variables to predict ozone concentrations.

AB - Vast amounts of data are being generated to extract implicit patterns of ambient air pollutants. This study attempts to investigate the behavior of daily maximum 8-hour ozone concentrations measured at 15 monitoring sites in the Dallas-Fort Worth area from June 1, 2002 to May 31, 2006. Time-series models were constructed to predict ozone concentrations. Regression trees were developed to study how meteorological variables impact ozone concentrations. The diagnostic test demonstrated the accurate predictability of the constructed time-series model. Further, regression trees identified important variables to predict ozone concentrations.

KW - Ozone prediction

KW - Time series analysis

KW - Tree-based model

UR - http://www.scopus.com/inward/record.url?scp=44949207373&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44949207373&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:44949207373

SP - 670

EP - 675

T2 - IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World

Y2 - 19 May 2007 through 23 May 2007

ER -

Statistical modeling for the prediction of ozone concentrations in the dallas-forth worth area

Abstract

Other

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this