Coupling of neural network and dispersion models: a novel methodology for air pollution models. (11th May 2004)

• Record Type: Journal Article
• Authors: Pelliccioni, A.; Gariazzo, C.; Tirabassi, T.
• Is Part Of: International journal of environment and pollution. Volume 20:Number 1-6(2003)
• Page Start: 136
• Page End: 146
• Publication Date: 2004-05-11
• Subjects: air pollution -- air pollution models -- neural networks -- modelling -- dispersion models; Environmental policy -- Periodicals; Environmental engineering -- Periodicals; Environmental sciences -- Periodicals; Pollution -- Periodicals; 363.73
• Languages: English
• ISSNs: 0957-4352

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.