Prediction of Air Pollution Using Random Forest

Abstract

The aim of this project is to use a heterogeneous ensemble of differential evolution with random forest technique for  pollution prediction in Indian Capital. This method is different from existing work like mathematical model for the prediction of air quality of the region. We will Random forest algorithm for the prediction of air pollution in the urban region of New Delhi. A random forest algorithm is an ensemble method for regression and classification. We will determine the presence of air pollutants like carbon mono-oxide, carbon dioxide, nitrogen , sulphar dioxide, ozone, pm2.5, pm10. We will also use many more data for our purpose like meteorology data, point of interest data, traffic and road data for determining air quality of New Delhi. This paper will work on an algorithm called Random Forest, the random forest algorithm will be used as classification. The main aspect for determining the air pollution of any region, we will need to know about the real-time information of air quality from monitoring stations like PM2.5[25], PM10, NO2 as these are main air pollutants which causes more damages to human beings and our environment. If we talk about air quality then particulate matter is main cause effecting the health of humans and management of city. It also affect government policies. However, in big cites there are very few monitoring stations of air quality.So, in our paper we will talk about Random forest Technique to predict and Measure Pollution This algorithm is also used for data training and prediction.