Data-Driven Urban Resilience for Sustainable Cities: Leveraging Smart Infrastructure and Predictive Analytics

Abstract

Urban areas are increasingly challenged by climate-related hazards, infrastructure stress, and rapid urbanization, making urban resilience a critical component of sustainable city development. In response, cities are adopting data-driven approaches that combine smart infrastructure and predictive analytics to enhance their capacity to anticipate, absorb, and recover from disruptions. This research examines how smart infrastructure systems and data-driven predictive analytics contribute to urban resilience and long-term sustainability. The study adopts a conceptual and analytical approach, synthesizing established theories of urban resilience, smart city development, and urban computing to develop an integrated framework for data-driven resilience planning. Smart infrastructure, supported by Internet of Things technologies and real-time data platforms, enables continuous monitoring of urban systems, improving infrastructure reliability, service efficiency, and adaptive capacity. Predictive analytics further enhances resilience by enabling forecasting, early warning, and proactive decision-making across critical urban sectors such as transportation, environmental management, and infrastructure maintenance. The proposed framework demonstrates how the interaction between digital infrastructure, analytics capabilities, and governance coordination can strengthen urban adaptability and sustainability performance. The findings highlight that data-driven urban resilience is not solely a technological outcome but also depends on effective institutional coordination and strategic integration of analytics into urban planning processes. This study contributes to the growing body of smart city and resilience research by clarifying the mechanisms through which data-driven systems support sustainable and resilient urban transitions.