OO 
IAPRS & SIS, Vol.34, Part 7, "Resource and Environmental Monitoring", Hyderabad, India,2002 
   
Knowledge Base 
  
  
007/655 
  
  
  
NA 
  
  
Input * Y» E 
layer Learning/Training layer output 
layer 
  
  
Figure 2. Modified Artificial Neural Network 
weights, accumulation and learning rule modification. 
Typically for the problem chosen the inputs and output 
measurements, have so many factors influencing them like for 
example 
data may be fuzzy i.e. imprecise and uncertain 
data may be measurements, which have inherent 
uncertainties or errors-- for input 
Similarly the set of data used for mapping may be fuzzy or 
imprecise or if they are also measurements, may contain errors 
and uncertainties. So additional rules and logic were added to 
ANN in the form of "if....then" rules or using simple predicate 
logic and later tested for training. 
In the present study the inputs have been chosen as measured 
values of temperature, wind speed and wind direction and the 
outputs are measurements of pollutant concentration of SO, 
NOx etc. As mentioned earlier both input and output values are 
measurements with associated inadequacies. Further the input 
measurements are location, season and weather-at-the-time-of- 
measurement dependent along with the usual errors of 
measurement and so are the pollutant measurements of output. 
Now, some a-priori knowledge is needed to reflect overall 
nature of inputs as well as to modify the weights based on one 
or two cycles of training the ANN for some sets of data. 
Present architecture used for ANN is shown below in Fig. 3. 
Details of one set of input data and output are shown in Table 1. 
This is typical as it pertains to a certain hour, in an hourly 
measurements on a particular date, of a certain month and year. 
The total sets of data if one groups on hourly basis are 24 per 
day and on daily basis it is 30 or 31 for a month. Now the 
ANN is ready for application and using this and sets of data 
typical training weights were obtained even if inputs are 
scattered on a weekly or monthly basis. 
  
  
  
Inpuis 
Knowledge for weights, threshold, learning 
  
  
Outputs 
  
-Lemp. 
-wind 
speed 
-wind 
directn. 
* fuzzy 
“errors 
Tocation 
"Seas onal 
Wweather-on- 
   
  
Concn. of 
pollutants 
A such as 
S302, NOx 
eic. 
  
  
  
  
  
  
  
  
the day Learning and Training layer 
  
  
  
  
  
Figure 3. Artificial Neural Network for the problem