The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing2008 
Input Hidden Output 
Fuzzv Slone 
Fuzzy Distance to Road 
Fuzzy Distance to 
Service Centre 
Fuzzy Distance to 
Administrative Area 
Fuzzy Distance to 
Residential Area 
Figure 4. Application of neural nets in determination 
relationship between the driving variable grids, output and 
network files. 
In order to prepare input files for SNNS software in the 
required format, all data layers need to be exported to ASCII 
files. Each cell in ASCII files was presented to the network will 
have a number assigned to it based on the cells relationship 
with the variables and the urbanization process. The higher 
numbers represent the neural network’s prediction of a more 
likely transition of that cell to urban area. 
The pattern file contained information from the 5 final input 
grids and one output file so that each line in the pattern file 
corresponds to one location. The output layer, on its turn, has 
only one neuron that corresponds to the map of transition 
probabilities for the considered type of land use change. In the 
training data set, the desired (target) value in the output layer is 
recorded as 1 for a cell that underwent a change in its land use, 
and 0 for a cell that had no change. In the extraction of the 
training data set for each network, large rectangles were 
delimited within the study area containing representative 
samples of the ranges of distances (for the maps of variables) 
and of land use change. In each cycle one complete 
presentation of all training cells to the network was performed, 
mean squared error generated by SNNS and each cycle was 
stored in a file for analysis. Based on the analysis, it was 
concluded that about 6400 cycles were adequate to stabilize the 
error level to a minimum value. It can be concluded that not 
only the number of iterations with neuro-fuzzy approach 
decreases, but also accuracy of land use change model 
improves. For testing, SNNS used the pattern file and the 
network file to generate an output file of activation values. The 
output file contains values ranging from 0.0 (no likelihood of 
changing to urban area) to 1.0 (highest likelihood of changing 
to urban area). 
3.3 Accuracy assessment of the GIS based neuro-fuzzy 
approach 
In order to evaluate the results, the predicted land use changes 
were overlaid to the observed changes in land use from 1980 to 
2000. According to this overlay, four categories were achieved. 
In two categories one and four, there is no error; because 
observed changes and predicted changes have the same results. 
However, the other two categories, two and three, had same 
error, because the observed changes and predicted changes 
have contradicted results. For this reason, we need Kapa 
coefficient to assess accuracy of the mode which uses all of the 
four categories for assessment. Kapa coefficient was calculated 
for this model equal to 78% which seems reasonable. A layer 
was created with the following codes at Table 1: 
1 
No observed change and no predicted change 
2 
Observed change but not predicted by the model 
3 
No observed change but change predicted by the model 
4 
Observed change and predicted change 
Table 1: Coding of predicted layer 
GIS was used to compare cells that were predicted to be the 
transition to urban areas (according to the model output) with 
the cells that actually did transition during the study period. For 
assessing the performance of the model, percentage of cells 
falling into this category was divided by the actual number of 
cells which have been changed to obtain a percent correct 
match (PCM) metric (Pijanowski et al., 2000). 
GIS was used to determine that 1145 cells changed into urban 
class in Tehran Metropolitan Area during the 20-year period 
1980-2000. Thus, 2073 cells were selected from the output file 
that had the greatest change likelihood values; these cells were 
then classified as new urban areas. The test was completed by 
comparing those cells that were observed as the changed areas, 
with those cells with the highest likelihood of the change, based 
on the model. The Percent Correct Metric (PCM) is the number 
of 4’s divided by the number of cells that were in transition. We 
typically picked the cycle which gives us the best PCM for our 
region. Neuro-fuzzy approach undertaken reaches 72% 
thematic accuracy. 
4. RESULTS 
ANN proved to have the capability to model non-linear features 
and handles well the uncertainties of spatial data. The 
methodology described in this paper showed the potential of 
implementing NN algorithms as a tool to predict the land use 
change based on historical satellite images. The numbers of 
cells indicating land use change have been equal to 1145. This 
number matches the number of cells between 1980 and 2000 
that actually transitioned to urban and were not part of the 
exclusionary layer. The actual recorded urban growth to the 
neural network’s prediction has been compared. In the case of 
the Tehran Metropolitan Area, 800 km 2 of non-urban lands 
were converted to urban areas over the 20-year interval 
between 1980 and 2000. 
Having urban land use change between 1980 and 2000 and 
assuming the existence of the same rate of urban change, urban 
land use of Tehran has been predicted for 2020. Based on a 
concurrent study in land use change detection in the Tehran 
Metropolitan Area between 1980 and 2000, it is clear that the 
land use change in this region for this time period is 
concentrated along west of the city. The land use change 
pattern to the south of the metropolitan area is also substantial 
but more dispersed (Figure 5).