Territorial spatial vulnerability assessment based on PSO-BP neural network: A case study in Shenzhen, China

Abstract

Studies on vulnerability, a fairly new research paradigm inspired by global environmental change, are expanding to support scientific decision-making for developing sustainable, resilient territorial space at the regional level. The goal of this research is to reveal the vulnerability of a human-environment coupled system and its interaction mechanisms amidst a changing environment. This study started with the vulnerability scoping diagram (VSD) conceptual model and built up a research framework of territorial spatial vulnerability according to the three structural elements of ecological protection, agricultural production and urban construction. Following the requirements for intelligent computing, we introduced particle swarm optimization and BP neural network learning algorithms to evaluate the vulnerability of Shenzhen's territory. We also introduced flow cytometry to analyze the mechanism of Shenzhen's territorial spatial vulnerability driven by human and natural forces. The results showed that the territorial spatial vulnerability of Shenzhen was low under various functional orientations. Among them, ecological protection-oriented territorial spatial vulnerability was high in the west and low in the east and followed an inverted U-shaped trend in the north-south direction. Agricultural production-oriented territorial spatial vulnerability was high in the east and low in the west, high in the north and low in the south. Urban construction-oriented territorial spatial vulnerability was high in the west and low in the east, high in the north and low in the south. Under the complex orientation of the gigantic human-environment coupled system, the vulnerability of territory was symmetrical and balanced in the north-south direction and fluctuated with an overall declining trend in the east-west direction. In vulnerability, ecological protection, agricultural production and urban construction systems independently accounted for 35.642%, 38.209%, and 26.149% of the territorial spatial vulnerability, respectively.