Author(s)

Amaury de Souza¹, Flavio Aristone¹, Ismail Sabbah²

¹Institute of Physics—Federal University of South Mato Grosso, Campo Grande, Brazil.
²Department of Natural Sciences, College of Health Sciences, the Public Authority for Applied Education and Training, Kuwait.

The estimation of the surface ozone concentration promotes the creation of data useful for planning the air quality forecast, which is a key element for the management of public health. The aim of this study is to develop an Artificial Neural Network (ANN) to estimate the concentration of surface ozone from daily climate data. ANN is an equivalent form of Feedforward Multilayer Perceptron whose data has been inserted from the daily concentration of measured ozone. In the intermediate and output layers activation functions like tan-sigmoid and linear have been used, respectively. The performance of the developed ANN is actually very good and it can be considered like part of the set of indirect methods to estimate the concentration of surface ozone. The proposed model may be used by governmental agencies as a tool to enable the public interventional actions during the period of atmospheric stagnation, when ozone levels in the atmosphere represent risks to the public health.

Neural Networks, Climate, Surface Ozone, Public Health, Air Quality

de Souza, A. , Aristone, F. and Sabbah, I. (2015) Modeling the Surface Ozone Concentration in Campo Grande (MS)—Brazil Using Neural Networks. Natural Science, 7, 171-178. doi: 10.4236/ns.2015.74020.