Application of Artificial Neural Networks to Predict the Impact of Traffic Emissions on Human Health

Artificial Neural Networks (ANN) have been essentially used as regression models to predict the concentration of one or more pollutants usually requiring information collected from air quality stations. In this work we consider a Multilayer Perceptron (MLP) with one hidden layer as a classifier of the impact of air quality on human health, using only traffic and meteorological data as inputs. Our data was obtained from a specific urban area and constitutes a 2-class problem: above or below the legal limits of specific pollutant concentrations. The results show that an MLP with 40 to 50 hidden neurons and trained with the cross-entropy cost function, is able to achieve a mean error around 11%, meaning that air quality impacts can be predicted with good accuracy using only traffic and meteorological data. The use of an ANN without air quality inputs constitutes a significant achievement because governments may therefore minimize the use of such expensive stations.