Title :
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
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;
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
DOI :
10.1109/CCDC.2009.5194848
