Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models

• Published in: Journal of intelligent systems 2021, Vol.28 (5), p.893-903
• Main Authors: Sankar Ganesh, S., Arulmozhivarman, Pachaiyappan, Tatavarti, Rao
• Format: Article
• Language: English
• Subjects: Air quality; air quality index; Algorithms; Artificial neural networks; Autoregressive models; Dependent variables; ensemble of predictors; Forecasting; gradient descent; Independent variables; Multilayer perceptrons; Neural networks; Nitrogen dioxide; Outdoor air quality; Radial basis function; Regression analysis; Regression models; Support vector machines
• ISSN: 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2017-0277

Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index (AQI), artificial neural networks (ANNs) trained with conjugate gradient descent (CGD), such as multilayer perceptron (MLP), cascade forward neural network, Elman neural network, radial basis function (RBF) neural network, and nonlinear autoregressive model with exogenous input (NARX) along with regression models such as multiple linear regression (MLR) consisting of batch gradient descent (BGD), stochastic gradient descent (SGD), mini-BGD (MBGD) and CGD algorithms, and support vector regression (SVR), are implemented. In these models, the AQI is the dependent variable and the concentrations of NO 2 , CO, O 3 , PM 2.5 , SO 2 , and PM 10 for the years 2010–2016 in Houston and Los Angeles are the independent variables. For the final forecast, several ensemble models of individual neural network predictors and individual regression predictors are presented. This proposed approach performs with the highest efficiency in terms of forecasting air quality index.