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

摘要

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 PM2.5) air pollution forecasting. The experiments were realized on datasets from the Airbase databases with PM2.5 hourly measurements. The main statistical parameters that were computed are root mean square error (RMSE) and mean absolute error (MAE).