Error functions for prediction of episodes of poor air quality

Abstract

Prediction of episodes of poor air quality using artificial neural networks is investigated. Logistic regression, conventional sum of-squares regression and heteroscedastic sum-of-squares regression are employed for the task of predicting real-life episodes of poor air quality in urban Belfast due to SO2. In each case, a Bayesian regularisation scheme is used to prevent over-fitting of the training data and to provide pruning of redundant model parameters. Non-linear models assuming a heteroscedastic Gaussian noise process are shown to provide the best predictors of pollutant concentration of the methods investigated. © Springer-Verlag Berlin Heidelberg 2002.