地理学报 ›› 2002, Vol. 57 ›› Issue (2): 159-166.doi: 10.11821/xb200202005

• 地理信息科学 • 上一篇    下一篇

基于神经网络的单元自动机CA及真实和优化的城市模拟

黎夏1, 叶嘉安2   

  1. 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
  • 收稿日期:2001-06-04 修回日期:2001-11-26 出版日期:2002-03-25 发布日期:2002-03-25
  • 作者简介:黎夏 (1962-), 男, 研究员。在香港大学获博士学位 (1993-96) 并完成博士后研究(1997-1998)。在国内外刊物上发表论文60多篇。E-mail: xlib@gis.sti.gd.cn; lix@graduate.hku.hk
  • 基金资助:

    国家自然科学基金项目(40071060) 和香港Croucher基金项目 (21009619)

Neural-network-based Cellular Automata for Realistic and Idealized Urban Simulation

LI Xia1, YEH Anthony Gar-On2   

  1. 1. Guangzhou Institute of Geography, Guangzhou 510070, China;
    2. Centre of Urban Planning and Environmental Management, University of Hong Kong, Hong Kong, China
  • Received:2001-06-04 Revised:2001-11-26 Online:2002-03-25 Published:2002-03-25
  • Supported by:

    National Natural Science Foundation of China, No.40071060; Hongkong Croucher Foundation, No.21009619

摘要:

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。当模型较复杂时,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依据。

关键词: 神经网络, 单元自动机, 城市模拟, 地理信息系统, 东莞

Abstract:

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.

Key words: neural networks, cellular automata, urban simulation, GIS, Dongguan