A new feature selection method based on machine learning technique for air quality dataset

Abstract

In the recent years, Air Pollution has become a matter of serious concern that leads to millions of premature deaths which motivates the researcher to predict the air quality in advance. Air Quality Index (AQI) always indicates the status of air quality. This index value is based on various pollutants like PM10, PM2.5, NO2, SO2, CO, O3, NH3, and Pb. Out of them PM2.5 nowadays is a major pollutant which is heavily affecting the quality of air. So, the major focus of this study is towards this parameter. PM2.5 is also dependent on various parameters, so in this study, a new feature selection method named as Causality Based Linear method has been proposed to select the most relevant parameters which affect the pollution. Experimental work has been carried out using existing machine learning techniques and proposed method on the air quality dataset of Delhi. It has been observed that the proposed method extracts wind speed, carbon monoxide and nitrogen dioxide as the key parameters and further accuracy of this method has been compared with the four existing methods where feature selection has not been considered. It has been found that the proposed method has given better accuracy with the key parameters.

Subject Classification:

• (2010) 68M12

Keywords:

• Air Quality Index
• Particulate Matter
• Causality Based Linear method
• Linear Regression
• Decision Trees
• Random Forest
• Neural Networks