System resilience assessment method of urban lifeline system for GIS

被引:27
作者
Huang, Wenjie [1 ]
Ling, Mengzhi [2 ]
机构
[1] Natl Univ Singapore, Fac Engn, Dept Ind Syst Engn & Management, Singapore, Singapore
[2] Natl Univ Singapore, Sch Design & Environm, Dept Architecture, Singapore, Singapore
关键词
System Resilience; Analytical Network Process; Entropy; Geographic Information System (GIS); DECISION-MAKING; INFRASTRUCTURE; QUANTIFICATION; ENTROPY; HAZARDS; MODELS;
D O I
10.1016/j.compenvurbsys.2018.04.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
System resilience is defined as the ability of a system to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. It is a key property to measure the robustness of complex and high coupling degree systems such as urban water supply system, electricity transmission system and waste disposal system. This paper proposes an integrated decision-making process to evaluate system resilience for urban lifeline systems. The decision-making process has two steps: First, the establishment of a system resilience indicator framework that contains five dimensional attributes, namely Materials and Environmental Resources, Society and Well-being, Economy, Built Environment and Infrastructure, and Governance and Institutes. Second, the development of a hybrid K-means algorithm, which combines entropy theory, bootstrapping and analytic network process. Using the real data of is resilient indicators data, this methodology identifies and classifies the resilience level of different regions supported by the lifeline systems. Finally, by combining with geographic information system, the calculation, visualization and classification of resilience leave can be realized. The results can serve as guidelines for governments to allocate resources and prevent large economic loss, especially in those low resilience level areas.
引用
收藏
页码:67 / 80
页数:14
相关论文
共 51 条
[41]  
Thomas L., 2006, DECISION MAKING ANAL
[42]   Resilience Quantification and Its Application to a Residential Building Subject to Hurricane Winds [J].
Tokgoz, Berna Eren ;
Gheorghe, Adrian V. .
INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE, 2013, 4 (03) :105-114
[43]   Design for resilience in infrastructure distribution networks [J].
Turnquist M. ;
Vugrin E. .
Environment Systems & Decisions, 2013, 33 (1) :104-120
[44]   Climate change impacts on water salinity and health [J].
Vineis P. ;
Chan Q. ;
Khan A. .
Journal of Epidemiology and Global Health, 2011, 1 (1) :5-10
[45]   Aggregating preference rankings using OWA operator weights [J].
Wang, Ying-Ming ;
Luo, Ying ;
Hua, Zhongsheng .
INFORMATION SCIENCES, 2007, 177 (16) :3356-3363
[46]   Probability weighted means as surrogates for stochastic dominance in decision making [J].
Yager, Ronald R. ;
Alajlan, Naif .
KNOWLEDGE-BASED SYSTEMS, 2014, 66 :92-98
[47]  
Yamagata Y., 2016, Urban Resilience
[48]   A generalized modeling framework to analyze interdependencies among infrastructure systems [J].
Zhang, Pengcheng ;
Peeta, Srinivas .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2011, 45 (03) :553-579
[49]   Assessing urban lifeline systems immediately after seismic disaster based on emergency resilience [J].
Zhao, Xudong ;
Cai, Hao ;
Chen, Zhilong ;
Gong, Huadong ;
Feng, Qilin .
STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2016, 12 (12) :1634-1649
[50]  
Zheng Guoguang, 2007, 16 CMA ORD