Urban carrying capacity of industrial cities to typhoon-induced Natechs: a cloud Bayesian model

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS
Kybernetes Pub Date : 2024-09-13 DOI:10.1108/k-03-2024-0774
Qiuhan Wang, Xujin Pu
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引用次数: 0

Abstract

Purpose

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Design/methodology/approach

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

Findings

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

Originality/value

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

工业城市对台风引发的 Natechs 的承载能力:云贝叶斯模型
设计/方法/方法利用灾害链理论和贝叶斯网络(BN),我们描述了自然灾害引发的工业安全事故(Natechs)的级联效应,确定了城市系统失效的关键节点。然后,我们利用变异系数和云贝叶斯网络提出了城市承载能力评估方法,构建了基础设施、人口和环境承载能力指标体系。该模型利用变异系数和云模型确定评估指标的区间值并对缺失数据节点进行加权。利用珠江三角洲地区的数据进行的案例研究验证了该模型。研究结果 (1) 珠江三角洲的城市发展在很大程度上依赖于人口承载能力。(2) 该地区的社会发展模式难以应对快速的工业增长。(3) 城市间人口承载能力差异显著,一些趋势与城市发展背道而驰。(4) 云 BN 在描述真实世界的模糊和随机情况方面优于经典的高木-菅野(T-S)门模糊方法。通过开发一个整合了云模型的 BN 风险评估模型,该研究解决了客观数据稀缺的问题,并减少了以往研究中严重依赖专家意见的主观性。结果表明,所提出的方法优于经典的模糊 BN。
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来源期刊
Kybernetes
Kybernetes 工程技术-计算机:控制论
CiteScore
4.90
自引率
16.00%
发文量
237
审稿时长
4.3 months
期刊介绍: Kybernetes is the official journal of the UNESCO recognized World Organisation of Systems and Cybernetics (WOSC), and The Cybernetics Society. The journal is an important forum for the exchange of knowledge and information among all those who are interested in cybernetics and systems thinking. It is devoted to improvement in the understanding of human, social, organizational, technological and sustainable aspects of society and their interdependencies. It encourages consideration of a range of theories, methodologies and approaches, and their transdisciplinary links. The spirit of the journal comes from Norbert Wiener''s understanding of cybernetics as "The Human Use of Human Beings." Hence, Kybernetes strives for examination and analysis, based on a systemic frame of reference, of burning issues of ecosystems, society, organizations, businesses and human behavior.
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