Artificial Neural Network for the Prediction of Particulate Matter (PM2.5)

In this paper, artificial neural network model developed for the prediction of PM2.5 particulate matter is discussed. Along with artificial neural network, multiple linear regression model is developed for the prediction. Both the techniques are compared for the prediction of PM2.5. The data for the training and testing is collected from OpenAq platform. The result shows that observed and predicted values are in close agreement. Artificial neural network is proved to be better than multiple regression model for the proposed application. Proposed method can also be used for data imputation technique for pollutant dataset.