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Journal of Environmental and Public Health
Volume 2017, Article ID 3131083, 12 pages
Research Article

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

1Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Jaipur, India
2Department of Mathematics, Swami Keshvanand Institute of Technology, Jaipur, India

Correspondence should be addressed to Akash Saxena; moc.liamtoh@anexas.hsakaa

Received 19 December 2016; Revised 3 June 2017; Accepted 17 July 2017; Published 15 August 2017

Academic Editor: Riccardo Buccolieri

Copyright © 2017 Akash Saxena and Shalini Shekhawat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.