Modified Cost Functions for Modelling Air Quality Time Series by Using Neural Networks

Abstract

In this paper a new Backpropagation algorithm appropriately studied for modelling air pollution time series is proposed. The underlying idea is that of modifying the error definition in order to improve the capability of the model to forecast episodes of poor air quality. Five different expressions of error definition are proposed and their cumulative performances are rigorously evaluated in the framework of a real case study which refers to the modelling of 1 hour average daily maximum Ozone concentration recorded in the industrial area of Melilli (Siracusa, Italy). Furthermore, two new performance indices to evaluate the model prediction capabilities referred to as Probability Index and Global Index respectively, are introduced. Results indicate that the traditional and the proposed version of Backpropagation perform quite similarly in terms of the Global Index which gives a cumulative evaluation of the model. However the latter algorithm performs better in terms of the percentage of exceedences correctly forecast. Finally a criterion to make the choice among various air quality prediction models is proposed.