Artificial Neural Network Approach for Forecasting Nitrogen Oxides Concentrations

Abstract

Abstract

This article presents the application of feed-forward multilayer perceptron (MP) networks to forecast hourly nitrogen oxides levels 24 h in advance. Input data were meteorological variables, average hourly traffic, and nitrogen oxides hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles) was analyzed to improve the models' prediction powers. Data were measured during 3 years at monitoring stations in Valencia (Spain) in two locations with high traffic density. The models' evaluation criteria were mean absolute error, root mean square error (RMSE), mean absolute percentage error, and correlation coefficient between observations and predictions. Comparisons of MP-based models proved that insertion of four additional seasonal input variables improved the ability of obtaining more accurate predictions, which emphasizes the importance of taking into account seasonal character of nitrogen oxides. When using seasonal components as predictors, root mean square error improves from 20.29 to 19.35 when predicting nitrogen dioxide and from 45.07 to 42.37 when forecasting nitric oxides if the model includes seasonal components at one study location. At the other location, RMSE changes from 23.76 to 23.05 when predicting nitrogen dioxide and from 33.94 to 33.10 for other pollutant's forecasts. Neural networks did not require very exhaustive information about air pollutants, reaction mechanisms, meteorological parameters, or traffic characteristics, and they had the ability of allowing nonlinear and complex relationships between very different predictor variables in an urban environment.