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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
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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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