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

Yutong Duan, Miao Yu, Weiyang Sun, Shiyang Zhang and Yunyuan Li ()
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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

Land, 2024, vol. 13, issue 6, 1-25

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.

Keywords: spatial vulnerability; ecological wisdom; BP neural network; genetic algorithm (GA); mountain city (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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