A comparative study of computational intelligence techniques applied to PM2.5 air pollution forecasting

Abstract

The paper presents the results of a comparative study performed between two computational intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) applied to particulate matter (fraction PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> ) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> hourly measurements. The main statistical parameters that were computed are root mean square error (RMSE) and mean absolute error (MAE).