Saxena Akash, Shekhawat Shalini
Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Jaipur, India.
Department of Mathematics, Swami Keshvanand Institute of Technology, Jaipur, India.
J Environ Public Health. 2017;2017:3131083. doi: 10.1155/2017/3131083. Epub 2017 Aug 15.
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 (SO, NO, PM, and PM). 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.
随着社会的发展以及人口的不断增加，对公共卫生的关注日益凸显。随着车辆数量的持续增长和工业发展，空气质量成为首要关注问题。出于这一担忧，人们提出了若干指标来指示污染物浓度。在本文中，我们提出了一个数学框架，以根据四种主要污染物（SO、NO、PM 和 PM）的个体浓度来制定累积指数（CI）。此外，还提出了一种基于监督学习算法的分类器。该分类器采用支持向量机（SVM）将空气质量分为两类，即良好或有害。该分类器的潜在输入是累积指数的计算值。在加尔各答、德里和博帕尔三个地点的真实数据上对该分类器的有效性进行了测试。结果发现，该分类器在对空气质量进行分类方面表现良好。
