AI-DRIVEN SMART CITIES: INTELLIGENT DISASTER RESPONSE AND URBAN RESILIENCE

In recent years, fast urbanization, variable climate, interrelated infrastructure
systems, and increased number and frequency of extreme events compel the
move from responsive disaster management to proactive and adaptive
resilient planning [cite:1]. Most literature in smart city disaster resilience to date has
focused on the enabling technologies and data systems. One review indicated that 72%
of the components of smart city resilience considered in the review were based on
information technology; clearly the reliance on sensing, analysis and digital
coordination has been key to urban resilient governance [cite:1]. In parallel, the rise of
AI, machine learning, IoT, Fog computing, predictive analytics are employed to enhance
real-time awareness, risk prediction, response coordination and essential urban service
continuity [cite:4].
This report discusses how AI-enabled smart city architecture could support
intelligent disaster response and long-term urban resilience through combined sensing,
edge intelligence, prediction and decision support [cite:2]. The report surveys the AI
smart city literature, proposes a layered architecture for disaster-resilient urban
system, elaborates on IoT plus Edge AI architecture that supports real-time operational
and predicts urban disaster situations using the predictive analytical approaches on
traffic, energy, environmental risk and emergency management [cite:6]. The chapter
then discusses various implementation obstacles of AI in smart city disaster resilience,
including privacy, interoperability, fragmented governance, digital inequity and model
trustworthiness, and potential future directions in this field, such as digital twin, multi
modal sensing, locally situated framework and human-centered AI governance [cite:2].