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

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

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 constru...

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Bibliographic Details
Main Authors: Cong Ou, Jianyu Yang, Zhenrong Du, Xin Zhang, Dehai Zhu
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Sustainability
Subjects:
urban-sprawl simulation
cellular automata
transition rules
unsupervised deep-learning algorithms
deep-belief networks
stacked denoising autoencoders
Online Access:https://www.mdpi.com/2071-1050/11/9/2464

Internet

https://www.mdpi.com/2071-1050/11/9/2464

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