A model for particulate matter (PM2.5) prediction for Delhi based on machine learning approaches

Abstract

Particulate matter (PM2.5) remains one of the most dominant contributors to air pollution in Delhi and its acute or chronic exposures have exerted serious health implications. Hence, it is necessary to accurately predict the magnitude of PM2.5 concentrations in order to develop emission reduction strategies for air quality management. In regard to this, few machine learning techniques have been applied to predict daily PM2.5 concentrations in Delhi. Two Different models i.e. SVM and ANN, were built on the inputs of various meteorological and pollutant parameters corresponding to 2-year period from 2016-18. Performance evaluation of the models for PM2.5 prediction has been executed and the results have been discussed. The results of this simulation exercise indicate that the ANN shows better prediction accuracy than SVM for PM2.5 prediction.