Stochastic perturbation and fuzzy distance transformation (FDT) have been widely introduced into the derivation of transition rules to improve the simulation capability of urban cellular automata (CA) models. However, their effects on urban growth simulation have not been revealed. In this article, we compare their effects on urban growth simulation for the city of Wuhan by a sensitivity analysis of the simulation accuracy and simulated urban patterns. We reveal the relationship between the two components and propose an optimized stochastic variable to improve the simulation capability of urban CA models. The results show that the stochastic perturbation is the main factor affecting the simulation accuracy and simulated urban patterns of Wuhan, in that it significantly reduces the simulation accuracy and increases the degree of fragmentation and the proximity and shape complexity of the simulation results. In addition, the change caused by the stochastic perturbation increases with its intensity. Although FDT can restrain the effect of the stochastic perturbation, its inhibitory effect becomes negligible as the stochastic perturbation intensity increases. The optimized stochastic variable can significantly reduce the negative impact of the original stochastic variable, and its improvement is more obvious when the stochastic perturbation intensity is higher.

中文翻译：

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分析随机扰动和模糊距离变换对武汉城市增长模拟的影响

随机扰动和模糊距离变换（FDT）已被广泛引入过渡规则的推导中，以提高城市细胞自动机（CA）模型的仿真能力。但是，它们对城市增长模拟的影响尚未揭示。在本文中，我们通过对模拟准确性和模拟城市模式的敏感性分析，比较了它们对武汉市城市增长模拟的影响。我们揭示了这两个组件之间的关系，并提出了一个优化的随机变量来提高城市CA模型的仿真能力。结果表明，随机扰动是影响武汉市模拟精度和模拟城市格局的主要因素，这样做会大大降低仿真精度，并增加碎片程度以及仿真结果的接近度和形状复杂度。另外，随机扰动引起的变化随强度的增加而增加。尽管FDT可以抑制随机扰动的影响，但随着随机扰动强度的增加，其抑制作用可以忽略不计。优化后的随机变量可以显着减少原始随机变量的负面影响，并且当随机扰动强度越高时，其改善越明显。随着随机扰动强度的增加，其抑制作用可忽略不计。优化后的随机变量可以显着减少原始随机变量的负面影响，并且当随机扰动强度越高时，其改善越明显。随着随机扰动强度的增加，其抑制作用可忽略不计。优化后的随机变量可以显着减少原始随机变量的负面影响，并且当随机扰动强度越高时，其改善越明显。