PM10 Diffusion Modeling By CNN And
Non-linear Predictive Functions
Author(s)
F. M. Raimondi, A. Lo Bue, M. C. Vitale & G. S. Amico
Abstract
In this paper a model for the forecast and control of atmospheric pollution caused
by particulate matter (PM10) is proposed. It is based on the use of cellular neural
networks (CNNs).
More precisely, the model is the result of the integration of the mass balance
equation and, at the same time, by the use of cellular neural networks (CNNs)
and Bayesian networks in the context of a planar grid which describes a whole
urban area; we considered the \“areal” sources conditioned by meteorological and
pollutant parameters.
The CNNs allow one to define a cellular system which gives the redefinition
of the mass balance equation trough a dynamic discrete rule (update) that
considers the contributions of the near cells.
Bayesian nets provide the forecast in a fixed time interval which will be used
for the determination of the pollutant amount in the interested area. Dynamics of
the single cell feel the effect of meteorological and environmental parameters;
contributions of these parameters are considered by means of some weights that
will be determined through the minimization of an error index that is a function
of the estimated data and the provisional data coming from the learning process
by Bayesian networks tested with values from monitoring stations. The results
obtained with this approach are quite interesting and the proposed model
produces innovative results because it takes advantage of the combination of two
models: CNN (for mesoscale topology) and Bayesian network (for the daily
forecast of the PM10 concentration).
1 Introduction
Controlling possible sources of atmospheric pollution and prevention critical
events has become a relevant problem in our increasingly degraded environment.
Keywords
Related Book
Other papers in this volume
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