AIR QUALITY ESTIMATION BY COMPUTATIONAL INTELLIGENCE METHODOLOGIES

Abstract

The subject of this study is to compare different computational intelligence ethodologies based on artificial neural networks used for forecasting an air uality parameter - the emission of CO2, in the city of Niš. Firstly, inputs of the O2 emission estimator are analyzed and their measurement is explained. It is nown that the traffic is the single largest emitter of CO2 in Europe. Therefore, a roper treatment of this component of pollution is very important for precise stimation of emission levels. With this in mind, measurements of traffic requency and CO2 concentration were carried out at critical intersections in the ity, as well as the monitoring of a vehicle direction at the crossroad. Finally, ased on experimental data, different soft computing estimators were developed, uch as feed forward neural network, recurrent neural network, and hybrid euro-fuzzy estimator of CO2 emission levels. Test data for some characteristic ases presented at the end of the paper shows good agreement of developed stimator outputs with experimental data. Presented results are a true indicator f the implemented method usability.

Keywords

Dates

  • Submission Date2012-05-03
  • Revision Date2012-07-16
  • Acceptance Date2012-07-20

DOI Reference

10.2298/TSCI120503186C

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