While cellular automata (CA) are considered an effective algorithm to model urban growth, their precise calibration can be challenging. The Shannon relative index (SRI) is an indicator of urban sprawl accounting for dispersion or concentration of built‐up/non‐built‐up areas. This study uses SRIs directly in the calibration of CA as patterns, applying a genetic algorithm (GA). Moreover, the kappa coefficient is used in the calibration process. CA was calibrated using data for 2001 and 2006 and validated using 2011 data to model urban growth in Shelby County, TN. Results indicate that the kappa coefficient achieves the highest value using the proposed method (89.48%) compared with a GA without patterns (86.15%, which underestimates 32.22 km²) or logistic regression (85.83%, which underestimates 36.76 km²). A more precise calibration of urban growth using the proposed method helps city planners to provide more realistic models for the future of the region.

中文翻译：

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基于模式的细胞自动机遗传算法和Shannon相对熵校准

虽然元胞自动机（CA）被认为是模拟城市增长的有效算法，但其精确校准可能具有挑战性。香农相对指数（SRI）是一个城市扩张指标，用于说明建成区/非建成区的分散或集中。这项研究使用遗传算法（GA），将SRI直接用于CA的模式校准。此外，卡伯系数用于校准过程。使用2001年和2006年的数据对CA进行了校准，并使用2011年的数据进行了验证，以对田纳西州谢尔比县的城市增长进行建模。结果表明，与不使用模式的GA（86.15％，其低估了32.22 km ²）或逻辑回归（85.83％，其低估了36.76 km ）相比，使用所提出的方法，卡帕系数达到了最高值（89.48％）。²）。使用建议的方法对城市增长进行更精确的校准有助于城市规划者为该地区的未来提供更现实的模型。