A New Cost Function for Air Quality Modeling


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Abstract

In this paper a new Back-Propagation (BP) algorithm cost function is appropriately studied for the modeling of air pollution time series. The underlying idea is that of modifying the error definition in order to improve the capabilities of this kind of models to forecast episodes of poor air quality. The proposed error definition can be regarded as a generalization of the traditional squared error cost function thanks to the presence of a parameter α which allows to obtain the ordinary BP as a special case when α=1. A criterion for choosing this parameter is stated based on setting a-priori a maximum level of allowable false alarms. The goodness of the proposed approach is assessed by means of case studies both on synthetic and measured air quality data.

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