提出了利用遥感数据，并采用支持向量机来确定元胞自动机非线性转换规则的新方法。元胞自动机在模拟复杂地理现象时，需要采用非线性转换规则。目前元胞自动机主要采用线性方法来获取转换规则，在反映复杂的非线性地理现象时有一定的局限性。以城市扩张的模拟为例，将模拟城市系统的主要特征变量映射到Hilbert空间后，通过SVM建立最优分割超平面，分割超平面的分类决策函数由径向基核（Radial Basis Kernel）构造。利用历史遥感数据校正超平面的决策函数，确定城市元胞自动机的非线性转换规则，计算出城市发展概率。利用所提出的方法，对深圳市1988-2010年的城市发展进行了模拟，取得了较理想的模拟效果。研究结果表明，基于SVM-CA模型的模拟精度比传统MCE方法模拟精度高，MoranⅠ指数与实际更为接近。

This paper presents a new method to simulate complex land use systems by integrating support vector machine(SVM),cellular automata,and GIS.Recently,cellular automata(CA) have been increasingly used to dynamically simulate urban growth and land use.There are many issues that should be solved in the simulation of this type of complex systems.One major problem is how to define transition rules using training data.Linear boundaries are often used to retrieve transition rules which define the probability of state conversion.However,many geographical phenomena are very complex and transition rules should be defined using nonlinear boundaries.In this study,a CA model based on the support vector machine(SVM) is developed using Visual Basic and ArcObjects of GIS.The GIS provides both data and spatial analysis functions for constructing SVM-CA model.Training data is conveniently retrieved from remote sensing and GIS database for calibrating and testing the model.The SVM method is used to transform the data from nonlinear boundaries in the original space to linear boundaries in the Hilbert space.The nonlinear transition rules can then be defined by using the functions of SVM.The SVM-CA model can be applied to the simulation of urban development.Complex global patterns can be generated from the local interactions with the SVM-CA model.This paper demonstrates that the proposed model can overcome some of the shortcomings of the existing CA models in simulating complex urban systems by using the nonlinear transition rules.The model has been successfully applied to the simulation of urban development in Shenzhen city of the Pearl River Delta.