A Neural Network Model For Three-hours-ahead
Prediction Of Ozone Concentration In The
Urban Area Of Palermo
Author(s)
U. Brunelli, V. Piazza & L. Pignato
Abstract
The purpose of this study is to use a recurrent neural network (Jordan model) to
forecast ozone concentrations (O3) with a short lead-time (1-3h) in the lower
atmosphere. The network has been trained using a time series that was recorded
between January 1st 2003 to December 31st 2003 and at two monitoring stations
in Palermo (Italy). Each input pattern is composed of twelve (hourly) values:
wind direction and intensity, barometric pressure, and ambient temperature;
respectively gathered at the meteorological stations in Bellolampo, Boccadifalco
and Castelnuovo. Ozone predictions are notoriously complex when using either
deterministic or stochastic models which explains why this model was developed
using a Neural Network. Neural Networks possess the ability to learn about nonlinear
relationships between the variables used. The model developed is a
potential tool for the predictions air quality parameters and it is superior to the
traditional stochastic model.
Keywords: emission sources, neural network, elemental concentrations.
1 Introduction
Vehicular traffic is a major source of air pollution, such as carbon monoxide
(CO), benzene, nitrogen oxides (NOx) and polycyclic aromatic hydrocarbons.
They are the principal causes of undesirable health effects and even premature
deaths. This phenomenon seems to be most relevant in urban areas like Palermo
where specific orographic and atmospheric conditions can generate pollutants’
accumulations. In this paper, the authors developed a statistical model to forecast
the ozone (O3) concentrations three hours ahead. Ozone is a colourless gas with a
Keywords
emission sources, neural network, elemental concentrations.
Related Book
Other papers in this volume
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