Evaluating trends of airborne contaminants by using support vector regression techniques

Author

Sotomayor-Olmedo, Artemio ; Aceves-Fernandez, M. Antonio ; Gorrostieta-Hurtado, Efren ; Pedraza-Ortega, J. Carlos ; Vargas-Soto, J. Emilio ; Ramos-Arreguin, J. Manuel ; Villaseñor-Carillo, U.

Author_Institution

Fac. de Inf., Univ. Autonoma de Queretaro, Queretaro, Mexico

fYear

2011

fDate

Feb. 28 2011-March 2 2011

Firstpage

137

Lastpage

141

Abstract

Monitoring, modeling and forecasting of air quality parameters are important topics in environmental and health research due to their impact caused by exposing to air pollutants in urban environments. The aim of this article is to show that forecast of daily airborne pollution using support vector machines (SVM) is feasible in regression mode. Results are presented using data measurements of Particulate Matter of aerodynamical size on the order of 10 and 2.5 micrograms (PMx) in London-Bloomsbury at south England.

Keywords

air pollution; environmental science computing; regression analysis; support vector machines; London-Bloomsbury; SVM; aerodynamical size; air pollutant; air quality parameter; airborne contaminant; daily airborne pollution forecasting; particulate matter; regression mode; south England; support vector regression technique; urban air pollution; Air pollution; Atmospheric modeling; Data models; Kernel; Polynomials; Support vector machines; Training; PMx; Particulate matter; Support Vector Machines; airborne pollution; forecast;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Communications and Computers (CONIELECOMP), 2011 21st International Conference on

Conference_Location

San Andres Cholula

Print_ISBN

978-1-4244-9558-0

Type

conf

DOI

10.1109/CONIELECOMP.2011.5749350

Filename

5749350