Using neural networks and cellular automata for modelling intra-urban land-use dynamics
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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Fuzzy similarity measures;
Document Type: Research Article
National Institute for Space Research (INPE), Remote Sensing Division—DSR, Sao Jose dos Campos, SP, Brazil
Federal University of Vicosa (UFV), Department of Forest Engineering—DEF, Campus Universitario, s/n-36571-000, Vicosa, MG, Brazil
National Institute for Space Research (INPE), Images Processing Division-DPI, Sao Jose dos Campos, Brazil
Federal University of Minas Gerais (UFMG), Centre for Remote Sensing—CSR/IGC, Belo Horizonte, MG, Brazil
Publication date: 2008-01-01
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