基于案例推理的元胞自动机及大区域城市演变模拟

元胞自动机（CA）被越来越多地用于复杂系统的模拟中。许多地理现象的演变与其影响要素之间存在着复杂的关系,并往往具有时空动态性。在研究区域较大和模拟时间较长时,定义具体的规则来反映这种复杂关系有较大的困难。为了解决CA转换规则获取的瓶颈问题,提出了基于案例推理（CBR）的CA模型,并对CBR的k近邻算法进行了改进,使其能反映转换规则的时空动态性。将该模型应用于大区域的珠江三角洲城市演变中。实验结果显示,其模拟的空间格局与实际情况吻合较好。与常规的基于Logistic的CA模型进行了对比,所获得的模拟结果有更高的精度和更接近实际的空间格局,特别在模拟较为复杂的区域时有更好的模拟效果。

Case-based Cellular Automaton for Simulating Urban Development in a Large Complex Region

The essential part of geographical cellular automata (CA) is to provide appropriate transition rules so that realistic patterns can be simulated. Transition rules can be defined by a variety of methods, such as multicriteria evaluation (MCE), logistic regression, neural networks, and data mining. The solicitation of concrete knowledge (transition rules) is often difficult for many applications. There are problems in representing complex relationships by using detailed rules. This study demonstrates that the case-based approach can avoid the problems of the rule-based approach in defining CA. The proposed method is based on the case-based reasoning techniques, which don't require the procedure of soliciting explicit transition rules. The knowledge for determining the state conversion of CA is inexplicitly embedded in discrete cases. The lazy-learning technology can be used to represent complex relationships more effectively than detailed equations or explicit transition rules. This paper presents an extended cellular automaton in which transition rules are represented by using case-based reasoning (CBR) techniques. The common k-NN algorithm of CBR has been modified to incorporate the location factor to reflect the spatial variation of transition rules. Multi-temporal remote sensing images are used to obtain the adaptation knowledge in the temporal dimension. This model has been applied to the simulation of urban development in the Pearl River Delta which has a hierarchy of cities. Comparison indicates that this model can produce more plausible results than rule-based CA in simulating large complex regions.