The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing2008 
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resulted from comparing output of the forecasted model against 
known land use changes. GIS has been used to overlay model 
forecasted and known land use changes to calculate the 
percentage of cells that the model correctly identifies as 
transition to urban area. 
3.1 Variables of land use change in GIS environment 
After preparation of land use maps for Tehran Metropolitan 
Area between 1980 and 2000 years, selection of appropriate 
parameters by expert people were performed. The first step in 
assessing the variables is to determine the factors affecting the 
suitable land use change on the basis of an analysis of existing 
studies. Input layers represent phenomena which may influence 
the model. In our case, we assume that the following 5 drivers 
will influence land use change in Tehran Metropolitan Area 
including: slope, proximity to residential area, commercial 
place, service centre and roads. Variables also include average 
yearly precipitation and elevation. 
These parameters were inserted to ArcMap software. Distance 
function has been used for road parameters. After these 
calculations, different layers in ArcMap have been stored. 
Exclusionary cells are cells which are not going to be included 
in the analysis. These driving predictor variables and the 
exclusionary zones were compiled in Arc/Info Grid format. 
Effective parameters require main considerations and criteria 
listed as follows: 
Absorbing Excursion Spaces: Absorbing excursion spaces 
contain distance from administrative and service centres. The 
distance each cell was from the nearest absorbing cell was 
calculated and stored as a separate variable grids. It is assumed 
that the costs of connecting to current absorbing services 
decrease with distance from urban areas. 
Transportation: It is another important factor which the 
distance of each cell had from the nearest road cell calculated 
and stored in separate coverage. The value of driving variable 
grids represented the potential accessibility of a location for 
new development. 
Landscape Features: Landscape topography is an influential 
factor contributing toward residential use. The amount of 
topographic variation surrounding each cell was estimated by 
calculating the standard deviation of all cell elevations within a 
4 km square area. Cells containing larger values reflect 
landscapes that contain a greater amount of topographic relief 
around them. 
Exclusionary Zones: This group includes limitations 
considered for land use change modelling in Tehran, which 
include existing urban areas, urban expansion plan, green 
spaces, historical and cultural centres, specific buffer for 
hospital and mosques, and other pious legacies mentioned in 
the comprehensive plan of Tehran. 
3.2 Neuro-fuzzy approach to simulate urban land use 
change 
This part shows that how the GIS based neuro-fuzzy approach 
has been used to simulate urban land use change. Output layers 
of previous stage were used as input for this stage. After the 
extended variables were introduced in different layers, we can 
apply fuzzy function in Spatial Analysis Tools for each layer 
separately. The main objective of this stage is fuzzification of 
layers that had been prepared in previous stage. Definition of 
fuzzy function which was used for fuzzification was performed. 
A new Toolbox in ArcGIS has been developed to perform the 
requested calculations. Accordingly, outputs from GIS 
calculation are the input files for the neural networks, the same 
spatial features and spatial rules were applied. For definition of 
fuzzy function, the views of some experts in addition to the 
parameters considered have been taken into account. For 
example, according to distance parameter, near distance fuzzy 
function was defined in ArcMap in spatial analysis tools while 
fuzzy driving variable grids are shown in Figure 3. 
Fuzzy Distance to Road Fuzz y Distance “> 
Administrative Area 
Fuzzv Slope 
Figure 3. Fuzzy driving variable grids produced by the GIS 
In order to perform prediction in neural networks, training and 
testing phase should be done carefully. In training phase 
presenting input values and adjusting the weights applied at 
each node has been considered. The testing presents a separate 
data set to the trained network independently to calculate the 
error rate. ANNs were applied to the prediction of land use 
change in four phases: (1) design of the network and of inputs 
from historical data; (2) network training using a subset of 
inputs; (3) testing of the neural network using the full data set 
of the inputs; and (4) using the information extracted from the 
neural network to forecast changes. In this study, the proposed 
neural networks have only three layers each - the input layer, a 
hidden layer and the output layer which Simple Back 
propagation algorithm was used as the learning process. SNNS 
(Stuttgart’s Neural Network Simulator) version 4.2 was used 
for design; training and prediction of the ANN (Zell et al., 1996) 
shown in figure 4. Difficult learning tasks can sometimes be 
simplified by increasing the number of hidden layers, but 
according to Gong (1996), a three-layer network can form any 
decision boundaries. The neural network has flexibility to 
choose optional number of inputs depending on the number of 
predictor variables presented to it, an equal number of hidden 
units as input units and a single output.