期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 22, 期 9, 页码 943-963出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658810701731168
关键词
neural networks; cellular automata; urban modelling; land-use dynamics; fuzzy similarity measures; town planning
Empirical models designed to simulate and predict urban land-use change in real situations are generally based on the utilization of statistical techniques to compute the land-use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non-parametric approaches. This work introduces a simulation experiment on intra-urban land-use change in which a supervised back-propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land-use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium-sized town in the Midwest of Sao Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985-1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures.
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