Urban Simulation Using Neural Networks and Cellular Automata for Land Use Planning

The paper presents a method for integrating neural networks, GIS and Cellular Automata (CA) that can be used in land use planning for simulating alternative development patterns according to different planning objectives. Neural networks are used to simplify model structures and facilitate the determination of parameter values. Unlike traditional CA models, the proposed model does not require users to provide transition rules, which may vary for different applications. Historical remote sensing data are used as the training data to calibrate the neural network. The training is robust because it is based on the well-defined back-propagation algorithm. Moreover, original training data are assessed and modified according to planning objectives to generate alternative development patterns.