TY - JOUR
TI - Air quality estimation by computational intelligence methodologies
AU - Ćirić Ivan T
AU - Ćojbašić Žarko M
AU - Nikolić Vlastimir D
AU - Živković Predrag M
AU - Tomić Mladen A
JN - Thermal Science
PY - 2012
VL - 16
IS - 12
SP - 493
EP - 504
PT - Article
AB - The subject of this study is to compare different computational intelligence  methodologies based on artificial neural networks used for forecasting an air  quality parameter - the emission of CO2, in the city of Niš. Firstly, inputs  of the CO2 emission estimator are analyzed and their measurement is  explained. It is known that the traffic is the single largest emitter of CO2  in Europe. Therefore, a proper treatment of this component of pollution is  very important for precise estimation of emission levels. With this in mind,  measurements of traffic frequency and CO2 concentration were carried out at  critical intersections in the city, as well as the monitoring of a vehicle  direction at the crossroad. Finally, based on experimental data, different  soft computing estimators were developed, such as feed forward neural  network, recurrent neural network, and hybrid neuro-fuzzy estimator of CO2  emission levels. Test data for some characteristic cases presented at the end  of the paper shows good agreement of developed estimator outputs with  experimental data. Presented results are a true indicator of the implemented  method usability. [Projekat Ministarstva nauke Republike Srbije, br.  III42008-2/2011: Evaluation of Energy Performances and br. TR35016/2011:  Indoor Environment Quality of Educational Buildings in Serbia with Impact to  Health and Research of MHD Flows around the Bodies, in the Tip Clearances and  Channels and Application in the MHD Pumps Development]