Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area

摘要: Abstract This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology the analysis, prediction interpretation of growth. The proposed model takes into account changes over time in population building use patterns. A GIS is handling spatial temporal data, performing contingency analysis mapping results. Spatial entities similar characteristics grouped together clusters by fuzzy c-means algorithm. Each cluster represents specific level growth development. two-layer feed-forward multilayer perceptron network then predict model, applied prefecture Attica, Greece, delineates current future evolution trends Athens metropolitan area, which illustrated maps dynamics. aims assist planners decision makers gaining insight transition from rural urban.