Title: Coupling of neural network and dispersion models: a novel methodology for air pollution models

Authors: A. Pelliccioni, C. Gariazzo, T. Tirabassi

Addresses: Ispesl-Dipia, Via Urbana 167, 00184 Rome, Italy. ' Ispesl-Dipia, Via Urbana 167, 00184 Rome, Italy. ' ISAC-CNR, Via Gobetti 101, 40129 Bologna, Italy

Abstract: Supervised neural net models and dispersion models are two important approaches for evaluating air pollution concentrations. The authors propose the development of an integrated model, in order to optimise the performances of each methodology. The concentrations evaluated by an air pollution model are coupled with a Neural Net (NN), so as to adjust the influence of important variables on dispersion models (which may produce systematic under- or over-prediction of measured concentrations). In particular, an optimised 3-Layer Perception with error-backpropagation learning rules is used to filter the air pollution concentrations evaluated using an operative analytical model that takes account of the vertical profiles of wind and turbulent diffusivity. The results show good performances of this methodology when applied to the Kincaid dataset.

Keywords: air pollution; air pollution models; neural networks; modelling; dispersion models.

DOI: 10.1504/IJEP.2003.004262

International Journal of Environment and Pollution, 2003 Vol.20 No.1/2/3/4/5/6, pp.136 - 146

Published online: 10 May 2004 *

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