Computational Intelligence-based PM2.5 Air Pollution Forecasting

Authors

  • Mihaela Oprea Automatic Control, Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania, 100680 Ploiesti, Bd. Bucuresti, 39
  • Sanda Florentina Mihalache Automatic Control, Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania, 100680 Ploiesti, Bd. Bucuresti, 39
  • Marian Popescu Automatic Control, Computers and Electronics Department Petroleum-Gas University of Ploiesti Romania, 100680 Ploiesti, Bd. Bucuresti, 39

Keywords:

computational intelligence, PM2.5 air pollution forecasting, ANFIS, ANN, ANN architecture identification

Abstract

Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27]a, in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM2.5) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.

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Published

2017-04-23

Issue

Vol. 12 No. 3 (2017): International Journal of Computers Communications & Control (June)

Section

Articles

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