Research on the assessment for air environment quality based on Support Vector Machine

Author

Haifeng, Wang ; Jun, Fang ; Chong, Gao

Author_Institution

Sch. of Bus. Manage., North China Electr. Power Univ., Baoding, China

fYear

2009

fDate

17-19 June 2009

Firstpage

4753

Lastpage

4757

Abstract

According to the theory of SVM, the models for the assessment of air environment quality were built and analyzed based on support vector classification and support vector regression. The conclusions are that the SVM is an easy and accurate method for the assessment of air environment quality and the model for the assessment of air environment quality is more accurate based on support vector classification. The models were used to assess the air environment quality of 11 cities of Hebei province in China and this article was also an empirical application example using SVM in the assessment of air environment quality.

Keywords

air pollution; environmental science computing; optimisation; pattern classification; regression analysis; support vector machines; China; Hebei province; SVM theory; air environment quality assessment; empirical application; optimization problem; statistical learning theory; support vector classification; support vector machine; support vector regression; Energy management; Environmental management; Lagrangian functions; Learning systems; Neural networks; Quality management; Static VAr compensators; Support vector machine classification; Support vector machines; Training data; Support Vector Machine (SVM); air environment quality; assessment;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5194848

Filename

5194848