A Forecast Model To Predict The Next Day’s
Maximum Hourly SO2 In The Site Of
Priolo (Siracusa)
Author(s)
U. Brunelli, V. Piazza & L. Pignato
Abstract
The purpose of this research is to develop a pure predictive model to forecast the
next day’s maximum hourly SO2 in the site of Priolo in two representative
monitoring stations. We globally propose a Recurrent Neural Network (RNN)
with an inherent dynamic memory to forecast the fluctuations of ground
concentration of SO2 pollutant. The model uses an available time series which
was recorded in the industrial site of Priolo, on the east coast of Sicily, Italy for
the period between 1st April 1998 and 12th December 2001. The inputs used to
train the neural network are three, where two are adimensional variable, which
are obtained by the meteorological data of the site and the third is the stability
classes of Thomas. This study has some important implications for health
warning systems environmental management in places with high pollution
concentration.
Keywords: air pollution, neural network, forecasting.
1 Introduction
Air pollution has a negative impact on the environmental and public health when
it occurs in the lower atmosphere. In this paper the results obtained by an
Artificial Neural Network to forecast the daily maximum SO2 ground
concentration are presented. A neural network is able to treat information that is
uncertain and incomplete like the human brain, and it has been utilised to model
complex non-linear functional relationships between predictor variables. The
Keywords
air pollution, neural network, forecasting.
Related Book
Other papers in this volume
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