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ModelOps for enhanced decision-making and governance in emergency control rooms

Author

Listed:
  • Kay Lefevre

    (Deakin University)

  • Chetan Arora

    (Deakin University)

  • Kevin Lee

    (Deakin University)

  • Arkady Zaslavsky

    (Deakin University)

  • Mohamed Reda Bouadjenek

    (Deakin University)

  • Ali Hassani

    (Deakin University)

  • Imran Razzak

    (Deakin University)

Abstract

For mission critical (MC) applications such as bushfire emergency management systems (EMS), understanding the current situation as a disaster unfolds is critical to saving lives, infrastructure and the environment. Incident control-room operators manage complex information and systems, especially with the emergence of Big Data. They are increasingly making decisions supported by artificial intelligence (AI) and machine learning (ML) tools for data analysis, prediction and decision-making. As the volume, speed and complexity of information increases due to more frequent fire events, greater availability of myriad IoT sensors, smart devices, satellite data and burgeoning use of social media, the advances in AI and ML that help to manage Big Data and support decision-making are increasingly perceived as “Black Box”. This paper aims to scope the requirements for bushfire EMS to improve Big Data management and governance of AI/ML. An analysis of ModelOps technology, used increasingly in the commercial sector, is undertaken to determine what components might be fit-for-purpose. The result is a novel set of ModelOps features, EMS requirements and an EMS-ModelOps framework that resolves more than 75% of issues whilst being sufficiently generic to apply to other types of mission-critical applications.

Suggested Citation

  • Kay Lefevre & Chetan Arora & Kevin Lee & Arkady Zaslavsky & Mohamed Reda Bouadjenek & Ali Hassani & Imran Razzak, 2022. "ModelOps for enhanced decision-making and governance in emergency control rooms," Environment Systems and Decisions, Springer, vol. 42(3), pages 402-416, September.
    • Handle: RePEc:spr:envsyd:v:42:y:2022:i:3:d:10.1007_s10669-022-09855-1
    DOI: 10.1007/s10669-022-09855-1
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    References listed on IDEAS

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    1. Ni Li & Minghui Sun & Zhuming Bi & Zeya Su & Chao Wang, 2014. "A new methodology to support group decision-making for IoT-based emergency response systems," Information Systems Frontiers, Springer, vol. 16(5), pages 953-977, November.
    2. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.
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    Cited by:

    1. Benjamin D. Trump & Igor Linkov, 2022. "Resilience and lessons learned from COVID-19 emergency response," Environment Systems and Decisions, Springer, vol. 42(3), pages 325-327, September.

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