IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i12p4194-d370581.html
   My bibliography  Save this article

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

Author

Listed:
  • Huafang Huang

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
    College of Economics & Management, Hefei Normal University, Hefei 230601, China
    Anhui Key Laboratory of Natural Disaster Process and Prevention, Wuhu 241002, China)

  • Xiaomao Wu

    (Department of Chemical and Biological Engineering, University of Sheffield, Sheffield, South Yorkshire S10 2TN, UK
    Anhui Province Energy Group Company Limited, Hefei 230011, China)

  • Xianfu Cheng

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
    Anhui Key Laboratory of Natural Disaster Process and Prevention, Wuhu 241002, China)

Abstract

In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km 2 . Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl.

Suggested Citation

  • Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2020. "The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4194-:d:370581
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/12/4194/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/12/4194/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jingjing Pei & Wen Liu, 2019. "Evaluation of Chinese Enterprise Safety Production Resilience Based on a Combined Gray Relevancy and BP Neural Network Model," Sustainability, MDPI, vol. 11(16), pages 1-13, August.
    2. Lin Chen & Chunying Ren & Bai Zhang & Zongming Wang & Mingyue Liu, 2018. "Quantifying Urban Land Sprawl and its Driving Forces in Northeast China from 1990 to 2015," Sustainability, MDPI, vol. 10(1), pages 1-18, January.
    3. Qian Zhang & Huaxing Zhang & Dan Zhao & Baodong Cheng & Chang Yu & Yanli Yang, 2019. "Does Urban Sprawl Inhibit Urban Eco-Efficiency? Empirical Studies of Super-Efficiency and Threshold Regression Models," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    4. L. Altieri & D. Cocchi & G. Roli, 2019. "Measuring heterogeneity in urban expansion via spatial entropy," Environmetrics, John Wiley & Sons, Ltd., vol. 30(2), March.
    5. Yslene Kachba & Daiane Maria de Genaro Chiroli & Jônatas T. Belotti & Thiago Antonini Alves & Yara de Souza Tadano & Hugo Siqueira, 2020. "Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
    6. Noland, Robert B. & Weiner, Marc D. & DiPetrillo, Stephanie & Kay, Andrew I., 2017. "Attitudes towards transit-oriented development: Resident experiences and professional perspectives," Journal of Transport Geography, Elsevier, vol. 60(C), pages 130-140.
    7. Xin Yan & Min Chen & Mu-Yen Chen, 2019. "Coupling and Coordination Development of Australian Energy, Economy, and Ecological Environment Systems from 2007 to 2016," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    8. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Syed Jawad Hussain Shahzad, 2017. "Can technology provide a glimmer of hope for economic growth in the midst of chaos? A case of Zimbabwe," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(2), pages 919-939, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ibraeva, Anna & Correia, Gonçalo Homem de Almeida & Silva, Cecília & Antunes, António Pais, 2020. "Transit-oriented development: A review of research achievements and challenges," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 110-130.
    2. Xuesong Sun & Zaisheng Zhang, 2021. "Coupling and Coordination Level of the Population, Land, Economy, Ecology and Society in the Process of Urbanization: Measurement and Spatial Differentiation," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    3. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    4. Li, Wenbo & Wang, Dongyan & Li, Hong & Wang, Jianguo & Zhu, Yuanli & Yang, Yuewen, 2019. "Quantifying the spatial arrangement of underutilized land in a rapidly urbanized rust belt city: The case of Changchun City," Land Use Policy, Elsevier, vol. 83(C), pages 113-123.
    5. Lu Liu & Jingjing Pei & Huiquan Wang & Yun Luo, 2023. "The Evaluation and Obstacle Analysis of Urban Safety Resilience Based on Multi-Factor Perspective in Beijing," Land, MDPI, vol. 12(10), pages 1-29, October.
    6. Zhanzhong Tang & Zengxiang Zhang & Lijun Zuo & Xiao Wang & Shunguang Hu & Zijuan Zhu, 2020. "Spatial Econometric Analysis of the Relationship between Urban Land and Regional Economic Development in the Beijing–Tianjin–Hebei Coordinated Development Region," Sustainability, MDPI, vol. 12(20), pages 1-21, October.
    7. Kyung Wook Seo & Sugie Lee, 2019. "Oxcart Route in the City: Tracking the Urbanization Process of an Agricultural Village in Korea," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    8. Jônatas Belotti & Hugo Siqueira & Lilian Araujo & Sérgio L. Stevan & Paulo S.G. de Mattos Neto & Manoel H. N. Marinho & João Fausto L. de Oliveira & Fábio Usberti & Marcos de Almeida Leone Filho & Att, 2020. "Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants," Energies, MDPI, vol. 13(18), pages 1-22, September.
    9. Batara Surya & Agus Salim & Hernita Hernita & Seri Suriani & Firman Menne & Emil Salim Rasyidi, 2021. "Land Use Change, Urban Agglomeration, and Urban Sprawl: A Sustainable Development Perspective of Makassar City, Indonesia," Land, MDPI, vol. 10(6), pages 1-31, May.
    10. Zhanzhong Tang & Zengxiang Zhang & Lijun Zuo & Xiao Wang & Xiaoli Zhao & Fang Liu & Shunguang Hu & Ling Yi & Jinyong Xu, 2021. "Spatial Evolution of Urban Expansion in the Beijing–Tianjin–Hebei Coordinated Development Region," Sustainability, MDPI, vol. 13(3), pages 1-23, February.
    11. Linda Altieri & Daniela Cocchi, 2021. "Spatial Sampling for Non‐compact Patterns," International Statistical Review, International Statistical Institute, vol. 89(3), pages 532-549, December.
    12. Khalid Mohammed Almatar, 2022. "Transit-Oriented Development in Saudi Arabia: Riyadh as a Case Study," Sustainability, MDPI, vol. 14(23), pages 1-10, December.
    13. Bao Meng & Xuxi Wang & Zhifeng Zhang & Pei Huang, 2022. "Spatio-Temporal Pattern and Driving Force Evolution of Cultivated Land Occupied by Urban Expansion in the Chengdu Metropolitan Area," Land, MDPI, vol. 11(9), pages 1-17, September.
    14. Paulo S. G. de Mattos Neto & Manoel H. N. Marinho & Hugo Siqueira & Yara de Souza Tadano & Vivian Machado & Thiago Antonini Alves & João Fausto L. de Oliveira & Francisco Madeiro, 2020. "A Methodology to Increase the Accuracy of Particulate Matter Predictors Based on Time Decomposition," Sustainability, MDPI, vol. 12(18), pages 1-33, September.
    15. Weimin Yan & Benhong Peng & Guo Wei & Anxia Wan, 2021. "Is There Coupling Effect between Financial Support and Improvement of Human Settlement? A Case Study of the Middle and Lower Regions of the Yangtze River, China," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    16. Yuegang Song & Ruibing Wu, 2022. "The Impact of Financial Enterprises’ Excessive Financialization Risk Assessment for Risk Control based on Data Mining and Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1245-1267, December.
    17. Chunguang Sheng & Guangyu Wang & Yude Geng & Lirong Chen, 2020. "The Correlation Analysis of Futures Pricing Mechanism in China’s Carbon Financial Market," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    18. Gulkaiyr Omurakunova & Anming Bao & Wenqiang Xu & Eldiiar Duulatov & Liangliang Jiang & Peng Cai & Farkhod Abdullaev & Vincent Nzabarinda & Khaydar Durdiev & Makhabat Baiseitova, 2020. "Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan," IJERPH, MDPI, vol. 17(1), pages 1-20, January.
    19. Qian Zhang & Huaxing Zhang & Dan Zhao & Baodong Cheng & Chang Yu & Yanli Yang, 2019. "Does Urban Sprawl Inhibit Urban Eco-Efficiency? Empirical Studies of Super-Efficiency and Threshold Regression Models," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    20. Luoman Pu & Jiuchun Yang & Lingxue Yu & Changsheng Xiong & Fengqin Yan & Yubo Zhang & Shuwen Zhang, 2021. "Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050," IJERPH, MDPI, vol. 18(3), pages 1-21, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4194-:d:370581. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.