Integration of Neural Networks and Cellular Automata for Urban Planning

作     者：Anthony Gar-on Yeh 

作者机构：Centre of Urban Planning and Environmental ManagementThe University of Hong Kong 

出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)

年 卷 期：2004年第7卷第1期

页      面：6-13页

学科分类：081603[工学-地图制图学与地理信息工程] 081802[工学-地球探测与信息技术] 07[理学] 08[工学] 070503[理学-地图学与地理信息系统] 0818[工学-地质资源与地质工程] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：FundedbytheCroucherFoundation HongKong 

主　　题：neural networks cellular automata GIS urban simulation urban planning 

摘      要：This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.