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Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541
Neural networks - a new mathematical tool for
air pollution modelling
M. Boznar, P. Mlakar
Jozef Stefan Institute, Jamova 39, SI-61000, Ljubljana, Slovenia
ABSTRACT
Modelling is a basis for better understanding and prevention of air
pollution. Many different mathematical techniques have been used
in this field. In this paper a new tool for air pollution modelling neural networks - will be
described. Its capabilities
and
disadvantages will be shown in the cases of wind forecasting and
reconstruction, and SC>2 pollution forecasting.
INTRODUCTION
Air pollution is one of the biggest problems of industrialised areas.
In order to better understand this phenomena, several different
modelling techniques have been applied in this field that are very
well known. Research in mathematical fields and in many other
modern fields of science also gave some rather new tools that can
be usefully employed in the field of air pollution modelling. One
such tool, that so far was very rarely used in air pollution
modelling, is that of neural networks, which are widely used in
other fields such as pattern recognition, speech recognition and
synthesis, financial forecasting, etc. The performance of neural
network based models will be shown with examples of wind
reconstruction and SO2 pollution forecasting in the Sostanj basin
(complex orography) in Slovenia. The Sostanj Thermal Power Plant
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Air Pollution Theory and Simulation
(STPP) is a single large source of SC>2 pollution (see Figure 1). A
modern Environmental Information System (EIS STPP) is in
operation around the plant, providing a huge data base suitable for
modelling (e.g. Lesjak^).
Figure 1: Map of the Sostanj basin with Thermal Power Plant and
automatic measuring stations.
ARTIFICIAL NEURAL NETWORKS
Artificial neural networks are mathematical structures that simulate
the structure and behaviour of the human brain. They are capable
of classification, of learning from the multitude of learning samples
without knowing any physical laws and equations, of generalisation
etc.
A neuron (see Figure 2) is the basic element of the neural
network. It receives information from the input links and produces
the output. Combining many neurons into a structure (see Figure
3) gives a neural network with learning and generalisation
capabilities. Neural networks differ from each other by their
structure (number and organisation of neurons), and type of
transfer function used in the neurons. In the process of learning
the neural network adjusts its interconnection weights (coy) in order
to minimise the error of predictions over the known training set of
data. When adjustment is made, the performance of the model can
be tested using data that were not included in the training set.
Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541
Air Pollution Theory and Simulation 261
OUTPUT
INPUTS
Figure 2: Typical neuron
INPUT
LAYER
_
HIDDEN
LAYER
OUTPUT
LAYER
NEURON
BASIC
ELEMENT
STRUCTURE
Figure 3: Typical structure of an artificial neural network
For our modelling we used a Perceptron neural network (e.g. that
of Lawrence*) with one hidden layer, and a sigmoid non-linear
transfer function that gives remarkable training and generalising
capabilities.
GROUND LEVEL WIND FORECASTING
Meteorological data, especially wind measurements are crucial for
the air pollution forecasting. If wind data were known in advance
(for example, a half an hour interval in advance), it would be
easier to predict plume movements. The first application was a
model that predicts wind measurements at the Graska gora station
(measurements at 10m). Separate models were built
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Air Pollution Theory and Simulation
21
31
case n u m b e r
Figure 4: Ground level wind direction prediction at Graska gora
station.
301
401
case number
predicted
measured]
fffH+H-fH ! 1 1 1 ! 1 1 1 i M 1 1 1 ! fti
41
61
case number
Figure 5: Ground
station.
level wind speed
prediction
81
at Graska
gora
Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541
Air Pollution Theory and Simulation 263
for wind speed and wind direction prediction. The inputs were the
wind speed and wind direction measurements at the stations of
Graska gora and Veliki vrh for the current half an hour interval
and for one measuring interval in the past for wind speed. For the
prediction of wind direction, measurements from the station at
Zavodnje were added.
The results are shown in Figure 4 and Figure 5. Wind direction
prediction seems to be a difficult problem, because the results were
not good at all. The wind speed prediction model performs better,
but the tendency to use the current measurement as the predicted
value can be seen from the detailed graph (Figure 5).
RECONSTRUCTION OF SODAR MEASUREMENTS
Another
useful application would be a model for reconstruction
Figure 6: Reconstruction of SODAR data (error of wind direction
at 50m)
(not forecasting) of missing SODAR data, because wind profiles are
essential for advance numerical models and
usually only
interpolations are used when there are no measurements available
at a particular level. For the Sostanj basin SODAR measurements
are available for one month in the spring of 1991, when a
measuring campaign was performed there (e.g. Elisei et. aU). As
the input for the model, ground level wind measurements at
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Air Pollution Theory and Simulation
different stations were taken (without SODAR measurements!). The
outputs were wind speed and direction at a particular SODAR
level. The results, as shown in Figure 6 and Figure 7, are
acceptable.
°
I
8
8
I
Figure 7: Reconstruction of SODAR data (wind direction; solid line
- measured; dotted line - reconstructed)
PREDICTION OF AMBIENT SO2 CONCENTRATIONS
Our most successful application is a neural network - based model
for ambient SO2 concentration prediction. The results were reported
in the literature (e.g. Boznar^), and at conferences (e.g. Mlakar^,
Mlakar&). The model predicts ambient SO2 concentrations at the
Zavodnje station for half an hour in advance. The inputs are
meteorological measurements (without SODAR data), ambient
pollutant concentration measurements at several stations and TPP
emission data. The results were very good. Some are shown in
Figure 8. Detailed analysis of the predictions shows that the model
predicts very good high peaks of concentration during thermal
inversion situations that are very difficult even to reconstruct with
other types of models.
Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541
Air Pollution Theory and Simulation
265
Testing patterns
measured
calculated
O
<-> m
<N
O
i
j
4.
o 4^—^—i—f—!
0
%
50
case number
%
100
Figure 8: Prediction of ambient SO2 concentrations for half an
hour in advance at Zavodnje station.
CONCLUSIONS
Three different applications of neural networks were shown to
encourage further research in this field. The most difficult problem
was prediction of wind speed and direction. Slightly better results
were obtained in reconstruction of SODAR vertical wind profiles.
The most suitable application for neural network based models
seems to be the prediction of pollutant concentrations. For all
these applications more effort should be put into finding proper
inputs for the model (input features), and finding information-rich
sets of learning and testing data.
REFERENCES
1. J. Lawrence Introduction
to Neural Networks California
Scientific Software, Grass Valley, 1991.
2. M. Boznar, M. Lesjak, P. Mlakar, A neural network-based
method for short-term predictions of ambient SO2 concentrations in
highly polluted industrial areas of complex terrain, Atmospheric
Environment, Vol.27B, pp. 221 - 230, 1993
Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541
266
Air Pollution Theory and Simulation
3. M. Lesjak, B. Diallo, P. Mlakar, Z. Rupnik, J. Snajder and B.
Paradiz., Computerised ecological monitoring system for the Sostanj
thermal power plant, in Man and his ecosystem, pp. 3-31 to 3-38,
Proc. 8th World Clean Air Congress, Hague, The Netherlands,
1989.
4. Elisei et. al., Experimental Campaign for the Environmental
Impact Evaluation of Sostanj Thermal Power Plant (1992), Jozef
Stefan Institute, ENEL/DSR/CRTN (Milano), CISE (Milano) Progress Report, (1992)
5. Primoz Mlakar, Marija Boznar, Martin Lesjak, Neural networks
predict pollution, Air Pollution Modelling and Its Application X,
ed. Gryning, Millan, pp. 659 to 660, Plenum Press, New York,
1994
6. P. Mlakar, M. Boznar, Short-term air pollution prediction on the
basis of artificial neural networks, pp. 545 to 552, Air Pollution II
Volume
1: Computer Simulation, Computational
Mechanics
Publications, Southampton, Boston, 1994