Air quality index forecasting using artificial neural networks - a case study on Delhi
by S. Sankar Ganesh; P. Arulmozhivarman; V.S.N. Rao Tatavarti
International Journal of Environment and Waste Management (IJEWM), Vol. 22, No. 1/2/3/4, 2018

Abstract: Air is the most vital constituent for the sustenance of life on earth. Air pollution is the major problem we have been facing. It is important to address this issue to lead a healthy life. Forecasting of air quality will contribute to a healthy society. In this paper, artificial neural network (ANN) predictors trained with conjugate gradient descent have been implemented to forecast air quality index (AQI) in a particular area of interest. Several neural network models such as multilayer perceptron (MLP), Elman, radial basis function and NARX were applied. In these neural network models, four major pollutant concentrations including NO₂, CO, O₃ and PM₁₀ for the year 2014 to 2016 in Delhi (India) were used to train each predictor. It can be concluded that, among all these models, radial basis function exhibited more accuracy in terms of measures of quality with mean absolute error (MAE) = 7.33, mean absolute percent error (MAPE) = 4.05%, correlation coefficient (R) = 0.993, root mean square error (RMSE) = 9.69 and index of agreement (IA) = 0.99.

Online publication date: Thu, 16-Aug-2018

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