关键词: 云模型, 贝叶斯网络, 组合权重, 城市承载力

Abstract: The rapid changes in global climate have heightened the frequency and intensity of natural disasters, posing a significant threat to industrial equipment exposed to such events. This vulnerability can lead to secondary industrial accidents, known as natural hazard triggered technological accidents (Natech), whose occurrence is steadily increasing. Accurately assessing the urban carrying capacity of industrial cities is a fundamental challenge in exploring the inherent development of cities. This assessment aims to enhance urban carrying capacity while maintaining the city’s intrinsic advantages. Currently, existing assessment methods for Natech often overlook the dynamic flow of risks within the Natech disaster chain, resulting in a lack of clarity in understanding the interactive relationships between different disaster events. Furthermore, research on carrying capacity primarily focuses on equipment and building structures, with limited emphasis on the urban system’s carrying capacity. Additionally, methods for evaluating urban or regional carrying capacity lack a systematic approach, with shortcomings in imperfect calculations for assessing the relevance of evaluation indicators, subjective weightings, and challenges in accurately quantifying multiple indicators. In this context, improving the evaluation model for urban carrying capacity in industrial cities and exploring key factors hold significant theoretical importance and practical value for the economic development and promotion of high-quality industrial growth in these cities.
In response to the prevailing issues of subjectivity, data scarcity, and uncertainties in indicator correlations within current evaluation models, this paper proposes a cloud Bayesian network approach integrating the coefficient of variation method. Utilizing a cloud generator to generate cloud model data, the model calculates the relative weights of unsupported nodes and obtains a conditional probability table. By objectively discretizing the states of each input node into three categories based on existing data within the Bayesian network, this model overcomes the subjective reliance on expert opinions prevalent in most urban carrying capacity studies. Simultaneously, the transformation of the fuzzy description of region numbers into a cloud map with specific numerical values addresses the challenge of lacking data support for intermediate nodes in the Bayesian network, completing the qualitative-to-quantitative conversion. Furthermore, by utilizing the cloud feature values of input nodes and their relative weights, the cloud feature values for relative nodes can be determined, establishing relative quantitative relationships based on weights. Finally, we select the Pearl River Delta industrial base, as the sample region. Three key indicators and twelve four-level assessment indicators are chosen to establish an index system for urban carrying capacity in the industrial area. The analysis spans 2009 to 2020, evaluating both overall the urban carrying capacity and individual indicators. The results indicate that the urban carrying capacity in the Pearl River Delta is consistently lower than the overall situation in Guangdong province. Moreover, the urban development in the Pearl River Delta demonstrates a significant dependence on population carrying capacity. The inherent social development model in the region proves ineffective in coping with the rapid growth of industry, highlighting an urgent need for improvements in waste disposal practices under large-scale industrial production. Furthermore, substantial disparities in the urban carrying capacity within the Pearl River Delta are observed, revealing instances where carrying capacity contradicts urban development trends.

Key words: cloud model, Bayesian network, combination weight, urban carrying capacity