A City-Level Integrated Case Base Design for Systemic Disaster Risk Management

Abstract

Urban disaster risks show multi-stage evolution and interconnected coupling features. Under time pressure, case-based reasoning (CBR) has emerged as a critical method for risk management decision making. Case-based reasoning tackles target case problems by leveraging solutions from similar historical cases. However, the current case base is inadequate for storing systemic risk cases, thus impeding CBR efficacy. This article presents a city-level integrated case base with a nested cross structure to facilitate the use of CBR in systemic risk management. It comprises a multi-layer vertical dimension and a multi-scale horizontal dimension. The vertical dimension is optimized to a four-layer (environment-hazard-object-aftermath) risk scenario classification system with taxonomy and fuzzy clustering analysis. The horizontal dimension is improved to a three-scale (network-chain-pair) risk association mode using event chain theory and association analysis. Hazard acts as the pivotal link between the two dimensions. An illustrative example displays the use process of the proposed case base, along with a discussion of its CBR-supported applications. Through the digital transformation, the suggested case base can serve government decision making with CBR, enhancing the city’s capability to reduce systemic risk.

References

[] Aamodt A, Plaza E. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 1994, 7(1): 39-59

[] AlHinai, Y.S. 2020. Disaster management digitally transformed: Exploring the impact and key determinants from the UK national disaster management experience. International Journal of Disaster Risk Reduction 51: Article 101851.

[] Bannour W, Maalel A, Ghezala HHB. Emergency management case-based reasoning systems: A survey of recent developments. Journal of Experimental and Theoretical Artificial Intelligence, 2023, 35(1): 35-58

[] Ben Ayed S, Elouedi Z, Lefevre E. An evidential integrated method for maintaining case base and vocabulary containers within CBR systems. Information Sciences, 2020, 529: 214-229

[] Chen, Q., Q.M. Zhang, and L. Zhai. 2018. Emergency classification level automatic judgment and warning system based on emergency action level of nuclear power plant. In Proceedings of the 2018 International Conference on Power System Technology, 6–8 Nov 2018, Guangzhou, China, 4632–4636.

[] Disaster Investigation Team of the State Council of China. 2022. Investigation report of the “7·20” extraordinary rainstorm in Zhengzhou City. Beijing: Disaster Investigation Team of the State Council of China (in Chinese).

[] Fan ZP, Li YH, Zhang Y. Generating project risk response strategies based on CBR: A case study. Expert Systems with Applications, 2015, 42(6): 2870-2883

[] Fan WC, Liu Y, Weng WG, Shen SF. Introduction to public safety science, 2013 Beijing Science Press in Chinese

[] Finnie G, Sun ZH. R5 model for case-based reasoning. Knowledge-Based Systems, 2003, 16(1): 59-65

[] Gill JC, Malamud BD. Hazard interactions and interaction networks (cascades) within multi-hazard methodologies. Earth System Dynamics, 2016, 7(3): 659-679

[] Helbing D. Globally networked risks and how to respond. Nature, 2013, 497(7447): 51-59

[] Huang WD, Wang Q, Ding BL, Cao J. Milošević D, Tang Y, Zu Q. A model based on a fuzzy petri net for scenario evolution of unconventional emergencies. Human centered computing. HCC 2019. Lecture notes in computer science, 2019 Cham Springer 458-467

[] Kassner M. Economic process as institutionalization of values. Karl Polanyi’s institutional theory and its ethical consequences. Annales Ethics in Economic Life, 2017, 20(6): 69-86

[] Kolodner JL. An introduction to case-based reasoning. Artificial Intelligence Review, 1992, 6(1): 3-34

[] Lewis J. Development in disaster-prone places: Studies of vulnerability, 1999 West Yorkshire ITDG Publishing

[] Li, M. 2012. Distribution projection selection of logistics enterprise based on fuzzy clustering analysis. In Proceedings of the 2012 World Automation Congress, 24–28 June 2012, Puerto Vallarta, Mexico, 1–4.

[] Li W, Jiang RG, Xie JC, Zhao Y, Zhu JW, Yang SY. Emergency management decision of urban rainstorm and flood disasters based on similar cases analysis. Natural Hazards, 2023, 116(1): 753-768

[] Mayr E, Bock WJ. Classifications and other ordering systems. Journal of Zoological Systematics and Evolutionary Research, 2002, 40(4): 169-194

[] Pan R, Yang Q, Pan SJ. Mining competent case bases for case-based reasoning. Artificial Intelligence, 2007, 171(16–17): 1039-1068

[] Rinaldi SM, Peerenboom JP, Kelly TK. Identifying, understanding, and analyzing critical infrastructure interdependencies. IEEE Control Systems Magazine, 2001, 21(6): 11-25

[] SCIOC (The State Council Information Office of China). 2023. The era of megacities: China Integrated City Index releases 2021 rankings. Beijing: The State Council Information Office of China.

[] Shen, L.L., J.P. Li, and W.L. Suo. 2022. Risk response for critical infrastructures with multiple interdependent risks: A scenario-based extended CBR approach. Computers and Industrial Engineering 174: Article 108766.

[] Shi PJ, Shuai JB, Chen WF, Lu LL. Study on large-scale disaster risk assessment and risk transfer models. International Journal of Disaster Risk Science, 2010, 1(2): 1-8

[] Sun ZH, Finnie G, Weber K. Case base building with similarity relations. Information Sciences, 2004, 165(1–2): 21-43

[] UNDRR (United Nations Office for Disaster Risk Reduction). 2017. Sendai Framework terminology on disaster risk reduction: Disaster risk. https://www.undrr.org/terminology/disaster-risk. Accessed 25 May 2024.

[] Wang, D.L., K.D. Wan, and W.X. Ma. 2020. Emergency decision-making model of environmental emergencies based on case-based reasoning method. Journal of Environmental Management 262: Article 110382.

[] Weichselgartner J, Pigeon P. The role of knowledge in disaster risk reduction. International Journal of Disaster Risk Science, 2015, 6(2): 107-116

[] Wisner B, Blaikie P, Cannon T, Davis I. At risk: Natural hazards, people’s vulnerability and disasters, 2004 London Routledge

[] Yu F, Li XY. Eslamian S, Eslamian F. Case-based reasoning for disaster management: Structure design for cascading disasters case base. Disaster risk reduction for resilience: Disaster risk management strategies, 2022 Cham Springer 295-314

[] Yu F, Fan B, Li XY. Improving emergency preparedness to cascading disasters: A case-driven risk ontology modelling. Journal of Contingencies and Crisis Management, 2020, 28(3): 194-214

[] Yu, F., B. Fan, C. Qin, and C. Yao. 2023. A scenario-driven fault-control decision support model for disaster preparedness using case-based reasoning. Natural Hazards Review 24(4): Article 04023040.

[] Yu F, Li XY, Han XS. Risk response for urban water supply network using case-based reasoning during a natural disaster. Safety Science, 2018, 106: 121-139

[] Yu F, Li XY, Sun QY. Design of the emergency case pedigree for scenario response. Systems Engineering-Theory & Practice, 2015, 35(10): 2596-2605 in Chinese

[] Yürüşen NY, Uzunoğlu B, Talayero AP, Estopiñán AL. Apriori and k-means algorithms of machine learning for spatio-temporal solar generation balancing. Renewable Energy, 2021, 175: 702-717

[] Zhang YQ, Su XN, Tang MW. Construction of the emergency case database based on the emergency evolution model. Library and Information Service, 2017, 61(18): 84-92 in Chinese

[] Zheng J, Wang YM, Zhang K, Liang J. A dynamic emergency decision-making method based on group decision making with uncertainty information. International Journal of Disaster Risk Science, 2020, 11(5): 667-679