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The international Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing2008 
Land use in 1980 
Land use 2000 
Residential Area 
Road 
Non urban 
Land use 2020 
S 
Figure 5. Land use change in Tehran Metropolitan Area from 
1980 to 2020 
forecasting the urbanization for specific years in the future 
based on ‘business as usual’ scenarios can be investigated. 
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5. CONCLUSIONS 
Models of land use change are tools to support the analysis of 
the causes and consequences of land use changes in order to 
better understand the functioning of the land use system and to 
support land use planning and policy. Models are useful for 
monitoring the complex suite of socio-economic and 
biophysical forces that influence the rate and spatial pattern of 
land use change and for estimating the impacts of changes in 
land use. This paper suggested a neuro-fuzzy approach which 
examines the relationship between 5 predictor variables and 
land use change. The model performs with a relatively high 
predictive ability (72%) at a resolution of 25*25 m. By 
applying this methodology to the Tehran Metropolitan Area, 
land use change, which resulted from predictor variables have 
been examined. The combined use of neuro-fuzzy and GIS 
proves to be an effective tool for land use change analysis. 
In order to simplify the model, we made several assumptions. 
First, we assumed that the pattern of each predictor variable 
remained constant beyond 1980. Second, the neural network 
itself was assumed to remain constant over time. Thus, the 
relative affect of each predictor variable is assumed to be stable. 
Finally, the amount of urban per capita undergoing a transition 
is assumed to be fixed over time. Given the availability of data, 
it is possible to relax many of these assumptions in order to 
examine the potential effect each of these assumptions have on 
the performance of model forecasts. The spatial resolution was 
set at 25m because of time constraints of the study, a higher 
spatial resolution would most likely yield better results for 
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