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Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth

Author

Listed:
  • Ye Zhou

    (School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China)

  • Feng Zhang

    (School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
    Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China)

  • Zhenhong Du

    (School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
    Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China)

  • Xinyue Ye

    (Department of Geography, Kent State University, Kent, OH 44240, USA)

  • Renyi Liu

    (School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
    Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China)

Abstract

Sustainable urban development is a focus of regional policy makers; therefore, how to measure and understand urban growth is an important research topic. This paper quantified the amount of urban growth on land use maps that were derived from multi-temporal Landsat images of Jiaxing City as a rapidly-growing city in Zhejiang Province from 2000–2015. Furthermore, a new approach coupled the heuristic bat algorithm (BA) and deep belief network (DBN) with the cellular automata (CA) model (DBN-CA), which was developed to simulate the urban expansion in 2015 and forecast the distribution of urban areas of Jiaxing City in 2024. The BA was proposed to obtain the best structure of the DBN, while the optimized DBN model considered the nonlinear spatial-temporal relationship of driving forces in urban expansion. Comparisons between the DBN-CA and the conventional artificial neural network-based CA (ANN-CA) model were also performed. This study demonstrates that the proposed model is more stable and accurate than the ANN-CA model, since the minimum and maximum values of the kappa coefficient of the DBN-CA were 77.109% and 78.366%, while the ANN-CA’s values were 63.460% and 76.151% over the 200 experiments, respectively. Therefore, the DBN-CA model is a potentially effective new approach to survey land use change and urban expansion and allows sustainability research to study the health of urban growth trends.

Suggested Citation

  • Ye Zhou & Feng Zhang & Zhenhong Du & Xinyue Ye & Renyi Liu, 2017. "Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth," Sustainability, MDPI, vol. 9(10), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1786-:d:113932
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    References listed on IDEAS

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    Cited by:

    1. Kaixuan Dai & Shi Shen & Changxiu Cheng & Sijing Ye & Peichao Gao, 2020. "Trade-Off Relationship of Arable and Ecological Land in Urban Growth When Altering Urban Form: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 12(23), pages 1-20, December.
    2. Linfeng Xu & Xuan Liu & De Tong & Zhixin Liu & Lirong Yin & Wenfeng Zheng, 2022. "Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model," Land, MDPI, vol. 11(5), pages 1-16, April.
    3. Yongjiu Feng & Jiafeng Wang & Xiaohua Tong & Yang Liu & Zhenkun Lei & Chen Gao & Shurui Chen, 2018. "The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    4. Cong Ou & Jianyu Yang & Zhenrong Du & Xin Zhang & Dehai Zhu, 2019. "Integrating Cellular Automata with Unsupervised Deep-Learning Algorithms: A Case Study of Urban-Sprawl Simulation in the Jingjintang Urban Agglomeration, China," Sustainability, MDPI, vol. 11(9), pages 1-20, April.

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