02048nas a2200241   4500000000100000000000100001008004100002260001200043653003400055653003100089653002000120653001600140653001900156653003200175100002000207700001900227245006700246856009900313300000900412490000600421520136500427022001401792       2020                            d  c03/202010aGeographic information system10aArtificial Neural Networks10aUrban Computing10aFuzzy Logic10aSoft Computing10aSpatial Information Systems1 aMostafa Borhani1 aNima Ghasemloo00aSoft Computing Modelling of Urban Evolution: Tehran Metropolis  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/03/ijimai20206_1_1_pdf_16471.pdf  a7-150 v63 aExploring computational intelligence, geographic information systems and statistical information, a creative and innovative model for urban evolution is presented in this paper. The proposed model employs fuzzy logic and artificial neural network as forecasting tools for describing the urban growth. This dynamic urban evolution model considers the spatial data of population, as well as its time changes and the building usage patterns. For clustering the spatial features, fuzzy algorithms were implemented to represent different levels of urban growth and development. Then, these fuzzy clusters were modeled by the multi-layer neural networks to estimate the urban growth. Based on this novel intelligent model, the current state of development of Tehran’s population and the future of this urban evolution were evaluated by empirical data and the achieved outcomes were detailed in qualitative charts. The input data-set includes four censuses with five-year intervals. Tehran's demographic evolution model forecasts the next five years with an overall accuracy of 81% and Cohen's kappa coefficient up to 74% beside the qualitative charts. These performance indicators are higher than the previous advanced models. The primary objective of this proposed model is to aid planners and decision makers to predict the development trend of urban population.  a1989-1660