Land change models are amongst the most widely developed tools for spatial decision support. Despite this progress, only a few models have been created thus far that simulate urban growth that incorporate two important aspects of uncertainty inherent to land use dynamics: fuzziness and roughness. Combining fuzziness and roughness into models will enhance the use of these tools for decision support. This study applied and evaluated a fuzzy-based approach to the feature selection effects on the accuracy of a land change model. Fuzzy rough set theory (FRST) was employed here as feature selection method and was integrated with a support vector regression (SVR) algorithm to simulate urban growth of Tabriz mega city in northwest Iran. In order to apply feature selection to a FRST algorithm, incoming data has been first fuzzified by an adaptive neural fuzzy inference system (ANFIS). To evaluate the application of FRST, SVR was used with and without FRST (SVR and SVR-FRST), while for performance evaluation logistic regression (LR) and kernelled LR (KLR) models were integrated with and without FRST (LR, LR-FRST, KLR, and KLR-FRST). The accuracy of the simulated maps of all models were evaluated by calculating the overall accuracy (OA), true positive rate (TPR), true negative rate (TNR), total operating characteristic (TOC) and their area under curve (AUC). The results showed that integrating FRST with the above-mentioned models enhanced the overall performances based on the above criteria. Among the above mentioned models, SVM-FRST and KLR-FRST yielded the best goodness of fit measures. Moreover, SVM-FRST with 83.6% OA, 41.6% TPR, and 90.4% TNR performs better than KLR-FRST with 82.4% OA, 37.4% TPR, and 89.8% TNR. However, KLR-FRST has more AUC, less green area destruction, more barren to urban areas conversion, and fast tuning process related to SVR-FRST. Finally, we suggest that KLR-FRST and SVR-FRST are, among those evaluated, the most appropriate models for urban growth modelling of the Tabriz mega city of Iran when considering uncertainty.

土地变化模型是用于空间决策支持的最广泛开发的工具之一。尽管取得了这一进展，但迄今为止，仅创建了少数几个模拟城市增长的模型，其中纳入了土地利用动态固有的不确定性的两个重要方面：模糊性和粗糙度。将模糊性和粗糙性结合到模型中将增强这些工具用于决策支持的使用。这项研究应用并评估了基于模糊的方法，用于特征选择对土地变化模型准确性的影响。本文采用模糊粗糙集理论（FRST）作为特征选择方法，并与支持向量回归（SVR）算法集成，以模拟伊朗西北部大不里士巨型城市的城市发展。为了将特征选择应用于FRST算法，传入的数据首先由自适应神经模糊推理系统（ANFIS）模糊化。为了评估FRST的应用，在有无FRST（SVR和SVR-FRST）的情况下使用SVR，而在有无FRST（LR，LR-FRST）的情况下，将逻辑回归（LR）和带内核的LR（KLR）模型集成在一起以进行性能评估。 ，KLR和KLR-FRST）。通过计算总体精度（OA），真正率（TPR），真负率（TNR），总工作特性（TOC）及其曲线下面积（AUC）来评估所有模型的仿真图的准确性。结果表明，基于上述标准，将FRST与上述模型集成在一起可以提高整体性能。在上述模型中，SVM-FRST和KLR-FRST产生了最佳的拟合度。此外，SVM-FRST的OA为83.6％，TPR为41.6％，90.4％的TNR的表现优于KLR-FRST，其OA为82.4％，TPR为37.4％，TNR为89.8％。但是，KLR-FRST具有更多的AUC，更少的绿地破坏，对城市地区的贫瘠转化以及与SVR-FRST相关的快速调整过程。最后，在考虑不确定性时，我们建议KLR-FRST和SVR-FRST是评估伊朗大不里士特大城市的城市增长模型的最合适模型。