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 Nis. 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.
Computational intelligence and transient thermal analysis methods for exploratory analysis of source air quality