An effective simulation of the urban sprawl in an urban agglomeration is conducive to making regional policies. Previous studies verified the effectiveness of the cellular-automata (CA) model in simulating urban sprawl, and emphasized that the definition of transition rules is the key to the construction of the CA model. However, existing simulation models based on CA are limited in defining complex transition rules. The aim of this study was to investigate the capability of two unsupervised deep-learning algorithms (deep-belief networks, DBN) and stacked denoising autoencoders (SDA) to define transition rules in order to obtain more accurate simulated results. Choosing the Beijing–Tianjin–Tangshan urban agglomeration as the study area, two proposed models (DBN–CA and SDA–CA) were implemented in this area for simulating its urban sprawl during 2000–2010. Additionally, two traditional machine-learning-based CA models were built for comparative experiments. The implementation results demonstrated that integrating CA with unsupervised deep-learning algorithms is more suitable and accurate than traditional machine-learning algorithms on both the cell level and pattern level. Meanwhile, compared with the DBN–CA, the SDA–CA model had better accuracy in both aspects. Therefore, the unsupervised deep-learning-based CA model, especially SDA–CA, is a novel approach for simulating urban sprawl and also potentially for other complex geographical phenomena.
Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China