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基于神经网络的单元自动机CA及真实和优化的城市模拟
Neural-network-based Cellular Atuomata for Realistic and Idealized Urban Simulation

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黎夏 ¹ 叶嘉安 ²

文摘	提出了一种基于神经网络的单元自动机（CA）。CA已被越来越多地应用在城市及其它地理现象的模拟中。CA模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂导，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考提供。
其他语种文摘	There is rapid development of CA models for simulation of land use patterns and urban systems recently. Traditional methods using multicriteria evaluation (MCE) have limitations because they only use a linear weighted combination of multiple factors for predictions. It cannot explain much of the non-linear variations presented in complex urban systems. It is most attractive that neural networks have the capabilities of nonlinear mapping which is critical for actual urban systems. The study indicates that improvement has been made by using the proposed model to simulate non-linear urban systems. The advantages of using neural networks are apparent. The method can significantly reduce much of the tedious work, such as the requirements for explicit knowledge of identify relevant criteria, assign scores, and determine criteria preference. Furthermore, variables used in spatial decision are always dependent on each other. General MCE methods are not suitable to handle relevant variables. Neural networks can learn and generalize correctly and handle redundant, inaccurate or noise data which are frequently found in land use information. Users don't need to worry about which variable should be selected or not. Knowledge and experiences can be easily learnt and stored for further simulation. General CA models also have problems in obtaining consistent parameters when there are many variables in the prediction. It is very time consuming in finding the proper values of parameters for CA models through general calibration procedures. This paper has demonstrated that neural network can be integrated in CA simulation for solving the problems in finding the values of parameters. Users don't need to pay great efforts in seeking suitable parameters or weights which are difficult to be determined by general CA methods. In the proposed method, the parameters or weights required for CA simulation are automatically determined by the training procedures instead of by users. It is convenient to embed the neural network in the CA simulation model based on the platform of GIS. The model is plausible in forecasting urban growth and formulating idealized development patterns. Different scenarios of development patterns can be easily simulated based on proper training using neural networks. Remote sensing data can be used to prepare training data sets for more realistic simulation. Based on planning objectives and development evaluation, original training data sets can be rationally modified to obtain different sets of adjusted weights through the training procedure of neural networks. These adjusted weights can be applied to the CA model in generating idealized patterns.
来源	地理学报 ,2002,57(2):159-166 【核心库】
关键词	神经网络 ; 单元自动机 ; 城市模拟 ; 地理信息系统 ; 东莞
地址	1. 广州地理研究所, 广州, 510070 2. 香港大学城市规划及环境管理研究中心, 香港
语种	中文
文献类型	研究性论文
ISSN	0375-5444
学科	测绘学
基金	国家自然科学基金 ; 香港裘槎基金
文献收藏号	CSCD:965959

参考文献共 13 共1页

引证文献 102 篇

1 汤君友试论元胞自动机模型与LUCC时空模拟土壤,2003,35(6):456-460,480
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2 罗平城市土地利用演化CA模型的扩展研究地理与地理信息科学,2004,20(4):48-51
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