Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China

Cong Ou, Jianyu Yang, Zhenrong Du, Xin Zhang and Dehai Zhu
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Cong Ou: College of Land Science and Technology, China Agriclutural University, No.17 Tsing Hua East Road, Haidian District, Beijing 100083, China
Jianyu Yang: College of Land Science and Technology, China Agriclutural University, No.17 Tsing Hua East Road, Haidian District, Beijing 100083, China
Zhenrong Du: College of Land Science and Technology, China Agriclutural University, No.17 Tsing Hua East Road, Haidian District, Beijing 100083, China
Xin Zhang: Institute of Electronics, China Academy of Sciences, No.19 North Fourth Ring West Road, Haidian District, Beijing 100190, China
Dehai Zhu: College of Land Science and Technology, China Agriclutural University, No.17 Tsing Hua East Road, Haidian District, Beijing 100083, China

Sustainability, 2019, vol. 11, issue 9, 1-20

Abstract: 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.

Keywords: urban-sprawl simulation; cellular automata; transition rules; unsupervised deep-learning algorithms; deep-belief networks; stacked denoising autoencoders (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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