Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations

The prediction of tropospheric ozone concentrations is very important due to the negative impacts of ozone on human health, climate and vegetation. The development of models to predict ozone concentrations is thus very useful because it can provide early warnings to the population and also reduce the number of measuring sites. The aim of this study was to predict next day hourly ozone concentrations through a new methodology based on feedforward artificial neural networks using principal components as inputs. The developed model was compared with multiple linear regression, feedforward artificial neural networks based on the original data and also with principal component regression. Results showed that the use of principal components as inputs improved both models prediction by reducing their complexity and eliminating data collinearity.