Air quality forecasting using convolutional neural networks

Abstract

Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deaths each year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from the damage which is caused by air pollution is one of the major issues for the global community. The prediction of air pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science to maximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this research is to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, which includes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposed system will be implemented in two steps; the first step will focus on data analysis and pre-processing, including filtering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters of each layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for the developed CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a high accuracy of 86.585%. The overall model is implemented using MATLAB software.