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Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

Author

Listed:
  • Yutong Duan

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Miao Yu

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Weiyang Sun

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Shiyang Zhang

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yunyuan Li

    (School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

Abstract

Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

Suggested Citation

  • Yutong Duan & Miao Yu & Weiyang Sun & Shiyang Zhang & Yunyuan Li, 2024. "Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China," Land, MDPI, vol. 13(6), pages 1-25, June.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:6:p:825-:d:1411151
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    References listed on IDEAS

    as
    1. Wilson, Geoff A. & Schermer, Markus & Stotten, Rike, 2018. "The resilience and vulnerability of remote mountain communities: The case of Vent, Austrian Alps," Land Use Policy, Elsevier, vol. 71(C), pages 372-383.
    2. Quoc Bao Pham & Sk Ajim Ali & Elzbieta Bielecka & Beata Calka & Agata Orych & Farhana Parvin & Ewa Łupikasza, 2022. "Flood vulnerability and buildings’ flood exposure assessment in a densely urbanised city: comparative analysis of three scenarios using a neural network approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1043-1081, September.
    3. Muhammad Ziaul Hoque & Shenghui Cui & Lilai Xu & Imranul Islam & Jianxiong Tang & Shengping Ding, 2019. "Assessing Agricultural Livelihood Vulnerability to Climate Change in Coastal Bangladesh," IJERPH, MDPI, vol. 16(22), pages 1-21, November.
    4. Shashidhar Kumar Jha & A. K. Negi & Juha M. Alatalo & R. S. Negi, 2021. "Socio-ecological vulnerability and resilience of mountain communities residing in capital-constrained environments," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(8), pages 1-23, December.
    5. Yining Ma & Suri Guga & Jie Xu & Yulin Su & Xingpeng Liu & Zhijun Tong & Jiquan Zhang, 2022. "Agricultural Vulnerability Assessment of High-Temperature Disaster in Shaanxi Province of China," Agriculture, MDPI, vol. 12(7), pages 1-20, July.
    6. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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