Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Yeganeh, Bijan, Shafie Pour Motlagh, Majid, Rashidi, Yousef, & Kamalan, Hamidreza (2012) Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmospheric Environment, 55, pp. 357-365.

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Abstract

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations.

The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Impact and interest:

15 citations in Scopus
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11 citations in Web of Science®

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ID Code: 68144
Item Type: Journal Article
Refereed: Yes
Keywords: CO concentration, Machine learning, Support Vector Machine, Partial Least Square, Hybrid models
DOI: 10.1016/j.atmosenv.2012.02.092
ISSN: 1352-2310
Subjects: Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ENVIRONMENTAL ENGINEERING (090700)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Atmospheric Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Atmospheric Environment, [VOL 55, (2012)] DOI: 10.1016/j.atmosenv.2012.02.092
Deposited On: 09 Mar 2014 23:39
Last Modified: 09 Mar 2014 23:39

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