Air Pollution Forecasting
Presented by
Sunil Ojha
Air Pollution Research Group
Why we need a Forecast
• To satisfy the needs of public information
• To further reduce and prevent exposure to
air pollutants
• To alert authorities, industry and the public
to take measures for emission reduction
• To increase public support for structural
measures
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Forecasting Methods
• Qualitative Methods
• Quantitative Methods
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Qualitative Methods
• Use the opinions of experts to subjectively
predict future events
– Such methods are often used when historical
data either are not available at all or are scarce
– These methods are often used to predict
changes in the pattern of historical data
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Quantitative methods
• Involves the analysis of historical data to
predict future values of a variable of interest
• Methods fall in two categories
– Univariate models
– Causal models
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Quantitative Methods
Univariate models
– Predicts future values of the variable of interest solely
on the basis of the historical pattern of that variable
Causal Models
– Predicts the future values of the variable of interest
based on the relationship between that and other
variables
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Air Pollution Forecasting
• Understand the nature of the pollutant by
determining
– How it forms?
– When it forms?
– How weather affects the pollutant?
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Statistical Modeling
• Involves determining the functional
relationship between the input and output
variables in a system
• Most common statistical method is linear
regression which has the general form :
Y= a + bx
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Statistical Modeling
Multiple Regression
– Used when you have more than one predictors
(independent variables)
– Takes the general form
Y = a + bx1+cx2+dx3
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Regression Analysis
• Fits a trend line using the least squares
approach
• Goodness of fit of this line is summarized
by the coefficient of determination (R2)
• Higher the value of R2 , better is the fit.
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Establishing independent variables
• Used for identifying the significant variables
• A correlation matrix is formed
• Higher correlation between the independent
variables should be avoided
• Significant variables are those having a higher
correlation with the dependent variable
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Neural Network flowchart
Output
Input Layer
Hidden Layer
General structure of the feed forward neural network
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Neural Network Modeling
Four step procedure:
1. The data is divided into two subsets
–
Training the data
–
Validating and testing the network
2. The input data are then scaled from 0 to 1
(this is often done by the neural network program)
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Neural Network Modeling
3. The architecture (or general layout) of the
network is determined by:
–
–
–
–
the number of input variables
the number of hidden layers
the number of neurons in each layer
the type of network to be used
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Neural Network Modeling
4. The network is trained by determining the
weights of neuron inputs
The training process
• Process of estimating optimum weights
for the links is known as the ‘training’ or
‘learning’ process
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Back Propagation Method
• A random value is assigned to start with
• Inputs are then propagated forward till it
reaches the output layer
• The error is then used to correct weights on
neurons that contributed most to the error
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Summary of the training process
•
•
•
•
•
•
•
Select input variables
Select network architecture
Initialize weights
Apply inputs to network
Measure error
Back-propagate errors and adjust weights
Repeat steps 4–6 a large number of times until the
network converges
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Development of neural network
forecasting model for predicting
Ozone concentrations
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Objective
To develop a neural network forecasting
model for predicting ozone concentrations
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Literature Review
• Surface temperature data has been used as a
surrogate variable by various modelers
• Most of the models developed has not been tested
on independent data sets
• Artificial neural network models perform better as
compared to statistical models if extreme values
exist
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Method
Time series of Ozone concentration can be
partitioned as :
X(t)=e(t)+S(t)+W(t)
where:
X(t) is the original time series, i.e., Ozone Concentration
e(t) is the long term trend component
S(t) is the true seasonal variation
W(t) is the short term variation
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Method
• Ozone concentration fluctuates due to:
– variation in meteorological conditions
– changes in emission of ozone precursor
chemicals
• The two different phenomena above should
be separated to get a better insight into the
changes of ozone concentrations with time
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Method
Kolmogorov-Zurbenko (KZm,p) filter
– low pass filter produced by repeated iterations of a
simple moving average
– Used to separate the deterministic portions (e and S)
from the short term variations
– User determines the final low pass filter
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Method
Each iteration of the moving average is defined by
k
Yi  1/ m  Xi  j
j  k
where m=2k+1
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Data
• Hourly observations of Ozone concentration
obtained from Ohio EPA
• The monitor at Cincinnati has been
considered
• Hourly observations for meteorological
variables obtained from NOAA
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Model development
• Three ANN models have been developed
– Filtered LN(Oz) vs. filtered Temp data
– Filtered Temperature data vs. actual temp data
– Filtered LN(Oz) data vs. actual LN(Oz)
• Feed forward network with
propagation algorithm has been used
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back
Model Development
• Hourly observations from 8 am to 9 pm
considered
• 1995-1997 data used for training the
network
• 1998 data used for validating
• Model is tested using 1999 data
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Correlation factors for different filters
0.800
0.700
(400,4)
(400,3)
Correlation factor
(365,3)
0.600
(365,1)
0.500
(365,2)
(400,2)
(400,1)
(275,1)
(200,1)
0.400
Series1
(0,0)
0.300
0.200
0.100
0
2
4
6
8
10
Iteration no.
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12
Conclusions
• Model developed using filtered data gives
better predictions
• The proposed model can be used only for
the specific monitor
• The proposed model is a better predictor of
hourly peak values
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