• Journal of Natural Resources
  • Vol. 35, Issue 12, 2875 (2020)
Jun-kai FAN and Jian-gang XU*
Author Affiliations
  • School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
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    DOI: 10.31497/zrzyxb.20201205 Cite this Article
    Jun-kai FAN, Jian-gang XU. Vulnerability assessment of urban agglomeration based on neural network model: A case study of Central Yunnan Urban Agglomeration[J]. Journal of Natural Resources, 2020, 35(12): 2875 Copy Citation Text show less

    Abstract

    Urban vulnerability is an effective measure to evaluate urban development resilience. At present, most studies on urban vulnerability focus on the special city and use the statistical method in China, and there is no urban vulnerability assessment method that is applicable to all the regional urban agglomerations. Taking the Central Yunnan Urban Agglomeration as an example, this paper develops an assessment system of urban vulnerability from three aspects of environmental system, economic system and social system. Entropy method and back propagation neural network model are used to evaluate the urban vulnerability of the study area from 2007 to 2016. The evaluation shows that the vulnerability of this urban agglomeration has a declining trend on the whole, but there is a big difference between the urban groups, which shows an unbalanced development. The evaluation results have reference significance for the planning and resilience development of the Central Yunnan Urban Agglomeration, and provide a scientific evaluation method for the study on the comprehensive development vulnerability of urban agglomeration.
    向指标:x'ij=xij-min{x1j,,xnj}max{x1j,,xnj}-min{x1j,,xnj}(1)

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    负向指标:x'ij=maxx1j,,xnj-xijmax{x1j,,xnj}-min{x1j,,xnj}(2)

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    pij=xiji=1nxij(3)

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    ej=-ki=1npijln(pij)(4)

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    dj=1-ej(5)

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    wj=djj=1mdj(6)

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    x'k=xkpk×100(7)

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    Jun-kai FAN, Jian-gang XU. Vulnerability assessment of urban agglomeration based on neural network model: A case study of Central Yunnan Urban Agglomeration[J]. Journal of Natural Resources, 2020, 35(12): 2875
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