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Cellular automata for simulating land use changes based on support vector machines

机译：基于支持向量机的元胞自动机，用于模拟土地利用变化

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Cellular automata (CA) have been increasingly used to simulate urban sprawl and land use dynamics. A major issue in CA is defining appropriate transition rules based on training data. Linear boundaries have been widely used to define the rules. However, urban land use dynamics and many other geographical phenomena are highly complex and require nonlinear boundaries for the rules. In this study, we tested the support vector machines (SVM) as a method for constructing nonlinear transition rules for CA. SVM is good at dealing with nonlinear complex relationships. Its basic idea is to project input vectors to a higher dimensional Hilbert feature space, in which an optimal classifying hyperplane can be constructed through structural risk minimization and margin maximization. The optimal hyperplane is unique and its optimality is global. The proposed SVM-CA model was implemented using Visual Basic, ArcObjects~R, and OSU-SVM. A case study simulating the urban development in the Shenzhen City, China demonstrates that the proposed model can achieve high accuracy and overcome some limitations of existing CA models in simulating complex urban systems.
机译：元胞自动机（CA）已被越来越多地用于模拟城市蔓延和土地利用动态。 CA中的一个主要问题是基于训练数据定义适当的过渡规则。线性边界已被广泛用于定义规则。但是，城市土地利用动态和许多其他地理现象非常复杂，并且需要规则的非线性边界。在这项研究中，我们测试了支持向量机（SVM）作为构建CA非线性转换规则的方法。 SVM擅长处理非线性复杂关系。它的基本思想是将输入向量投影到更高维的希尔伯特特征空间，在该空间中，可以通过结构风险最小化和边际最大化来构造最佳分类超平面。最优超平面是唯一的，其最优性是全局的。所提出的SVM-CA模型是使用Visual Basic，ArcObjects〜R和OSU-SVM实现的。通过对中国深圳城市发展进行模拟的案例研究表明，所提出的模型在模拟复杂的城市系统中可以达到较高的准确性，并克服了现有CA模型的某些局限性。

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