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Leveraging AI, IoT, and Predictive Analytics for Crisis Management and Urban Resilience in Dubai

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Title: Leveraging AI, IoT, and Predictive Analytics for Crisis Management and Urban Resilience in Dubai
Authors: Bin Kalli, Suhail
Committee Members: Khalil Al Hussaeni
Summary: The thesis will discuss how smart technologies could be incorporated in the crisis management structures of Dubai to make the city more resilient. As Dubai grows more urbanized, it experiences increased problems with managing its crisis especially in the high population density places, traffic congestion, utility disruption, and extreme weather. This paper discusses how artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics can be used to ameliorate the response to emergencies, manage the allocation of resources, and enhance the coordination between different agencies. The crisis management systems at Dubai are still ineffective despite the level of technologies employed because of traffic congestions and the lack of coordination. The proposed research will address the following question: How can smart technologies enhance crisis management in Dubai and enhance urban resilience? It was performed as a mixed-method strategy, which included secondary data in Dubai Pulse and primary data in surveys with major stakeholders in the Dubai Police, Fire Department, and Health Authority. It also analyzed comparative case studies of Singapore, Amsterdam and Barcelona in order to determine the best practices. The evidence indicates that victims have philosophical delays in the way they respond to the crisis, especially in the high-density locations such as Dubai Marina due to traffic jam and the absence of sufficient coordination. AI, IoT, and predictive analytics integration would potentially address the problem of delays and resource optimization as well as increase the level of agency collaboration. But obstacles like the cost involved, privacy issues and training are still present. To incorporate smart technologies into the Dubai infrastructure and enhance the city resiliency, this paper is suggesting the Smart Urban Resilience Framework (SURF).
URL: https://repository.rit.edu/theses/12482
Database: OpenDissertations
FullText Text:
  Availability: 0
Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.repository.rit.edu.theses.13617
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Leveraging AI, IoT, and Predictive Analytics for Crisis Management and Urban Resilience in Dubai
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bin+Kalli%2C+Suhail%22">Bin Kalli, Suhail</searchLink>
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  Data: <searchLink fieldCode="CO" term="%22Khalil+Al+Hussaeni%22">Khalil Al Hussaeni</searchLink>
– Name: Abstract
  Label: Summary
  Group: Ab
  Data: The thesis will discuss how smart technologies could be incorporated in the crisis management structures of Dubai to make the city more resilient. As Dubai grows more urbanized, it experiences increased problems with managing its crisis especially in the high population density places, traffic congestion, utility disruption, and extreme weather. This paper discusses how artificial intelligence (AI), the Internet of Things (IoT), and predictive analytics can be used to ameliorate the response to emergencies, manage the allocation of resources, and enhance the coordination between different agencies. The crisis management systems at Dubai are still ineffective despite the level of technologies employed because of traffic congestions and the lack of coordination. The proposed research will address the following question: How can smart technologies enhance crisis management in Dubai and enhance urban resilience? It was performed as a mixed-method strategy, which included secondary data in Dubai Pulse and primary data in surveys with major stakeholders in the Dubai Police, Fire Department, and Health Authority. It also analyzed comparative case studies of Singapore, Amsterdam and Barcelona in order to determine the best practices. The evidence indicates that victims have philosophical delays in the way they respond to the crisis, especially in the high-density locations such as Dubai Marina due to traffic jam and the absence of sufficient coordination. AI, IoT, and predictive analytics integration would potentially address the problem of delays and resource optimization as well as increase the level of agency collaboration. But obstacles like the cost involved, privacy issues and training are still present. To incorporate smart technologies into the Dubai infrastructure and enhance the city resiliency, this paper is suggesting the Smart Urban Resilience Framework (SURF).
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Smart Technologies
        Type: general
      – SubjectFull: Crisis Management
        Type: general
      – SubjectFull: Urban Resilience
        Type: general
      – SubjectFull: Predictive Analytics
        Type: general
      – SubjectFull: AI (Artificial Intelligence)
        Type: general
      – SubjectFull: IoT (Internet of Things)
        Type: general
      – SubjectFull: Emergency Response
        Type: general
      – SubjectFull: Resource Allocation
        Type: general
      – SubjectFull: Inter-Agency Coordination
        Type: general
      – SubjectFull: Dubai
        Type: general
      – SubjectFull: Smart Urban Resilience Framework (SURF)
        Type: general
      – SubjectFull: Traffic Congestion
        Type: general
      – SubjectFull: Utility Disruptions
        Type: general
      – SubjectFull: Weather Events
        Type: general
      – SubjectFull: Urban Planning
        Type: general
    Titles:
      – TitleFull: Leveraging AI, IoT, and Predictive Analytics for Crisis Management and Urban Resilience in Dubai
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bin Kalli, Suhail
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2026
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