Comparing Neural Networks And Transfer Function Models For Ozone
Forecasting
Author(s)
G. Latini, R. Cocci Grifoni, L. Magnaterra & G. Passerini
Abstract
Comparing neural networks and transfer
function models for ozone forecasting
G. Latini, R. Cocci Grifoni, L. Magnaterra & G. Passerini
Dipartimento di Energetics,
Universitd Politecnica delle Marche, Italy.
Abstract
Surface ozone concentrations are determined by complex interactions between
precursors and are triggered by meteorological conditions. Ozone
concentrations are, in fact, strongly linked to meteorological conditions in the
boundary layer and to land-sea breezes at coastal sites. The related relationships
are typically complex and nonlinear and might be better captured by dynarnical
models, namely Neural Networks and Transfer Function models.
Aim of our work is the identification of proper Transfer Function models
and the estimation of their parameters. Here we present an outline of the
methodology that was used to develop the air pollution forecast model for a
complex coastal valley. We also investigate the potential for using Neural
Networks, namely Multi-Layer Perceptron networks,
Keywords
Related Book
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