Forecasting Time Series AQI Using Machine learning of Haryana Cities Using Machine Learning

Abstract

In India and throughout the world, air pollution is becoming a severe worry day by day. Governments and the general public have grown more concerned about how air pollution affects human health. Consequently, it is crucial to forecast the air quality with accuracy. In this paper, Machine learning methods SVR and RFR were used to build the hybrid forecast model to predict the concentrations of Air Quality Index in Haryana Cities. The forecast models were built using air pollutants and meteorological parameters from 2019 to 2021 and testing and validation was conducted on the air quality data for the year 2022 of Jind and Panipat city in the State of Haryana. Further, performance of hybrid forecast model was enhanced using scalar technique and performance was evaluated using various coefficient metrics and other parameters. First, the important factors affecting air quality are extracted and irregularities from the dataset are removed. Second, for forecasting AQI various approaches have been used and evaluation is carried out using performance metrics. The experimental results showed that the proposed hybrid model had a better forecast result than the standard Random forest Regression, Support Vector Regression and Multiple Linear Regression.