Convergence of AI and urban emergency responses: Emerging pathway toward resilient and equitable communities

Sun, Li, Li, Haijiang ORCID: https://orcid.org/0000-0001-6326-8133, Nagel, Joseph and Yang, Siyao 2024. Convergence of AI and urban emergency responses: Emerging pathway toward resilient and equitable communities. Applied Sciences 14 (7) , 7949. 10.3390/app14177949

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Abstract

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2076-3417
Date of First Compliant Deposit:	6 September 2024
Date of Acceptance:	28 August 2024
Last Modified:	11 Sep 2024 13:45
URI:	https://orca.cardiff.ac.uk/id/eprint/171851

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