PUMA
Istituto di scienze dell'atmosfera e del clima     
Pelliccioni A., Gariazzo C., Tirabassi T. Coupling of neural network and dispersion models: a novel methodology for air pollution models. In: International Journal of Environment and Pollution, vol. 20 (1-6) pp. 136 - 146. Inderscience Enterprises, 2003.
 
 
Abstract
(English)
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.
DOI: 10.1504/IJEP.2003.004262
Subject Air pollution
Air pollution models
Neural net models


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional