Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.

精准模拟与预测城市扩张，对于管控城市化进程、明确城市扩张的时空趋势与分布特征至关重要。元胞自动机耦合马尔可夫链（Cellular Automata integrated Markov Chain，CA-MC）是当前该领域最常用的模型之一。然而，传统CA-MC模型生成的城市适宜性指数（urban suitability index，USI）图谱，要么受人为偏差干扰，要么无法准确反映驱动因子与城市扩张间潜在的非线性关联。为克服上述局限，本研究采用机器学习模型——人工神经网络（Artificial Neural Network，ANN）替代常规使用的层次分析法（Analytical Hierarchy Process，AHP）与逻辑回归（Logistic Regression，LR）耦合CA-MC模型。通过优化ANN生成USI图谱，并将其与CA-MC耦合以实现城市扩张元胞的空间分配。经卡帕系数（kappa）与模糊卡帕系数（fuzzy kappa）验证的模拟结果显示，ANN-CA-MC模型的性能优于其他各类耦合CA-MC建模方案。基于ANN-CA-MC模型，南奥克兰（South Auckland）的城市用地规模预计将在2026年扩张至1340.55公顷（ha），其扩张将以占用非城市用地为代价，主要来源为草地与裸露空地。未来大部分城市扩张将发生在规划的新增城市增长区内。