Forecasting with neural networks
Information and Management
International Journal of Geographical Information Science
PADS '12 Proceedings of the 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation
Parallelization of ensemble neural networks for spatial land-use modeling
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
A hybrid analytical-heuristic method for calibrating land-use change models
Environmental Modelling & Software
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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.