Abstract
Urban flood events are increasing in frequency and severity due to climate change and rapid urbanization, necessitating advanced tools for real-time risk management. Digital twin technology offers a promising paradigm by integrating real-time sensor data, simulation models, and decision support systems to enhance urban flood resilience. This paper proposes a comprehensive framework for developing digital twins tailored to urban flood resilience, enabling real-time simulation, predictive analytics, and informed decision-making. The framework integrates hydrodynamic models, machine learning algorithms, and cyber-physical system principles to create a synchronized virtual replica of urban drainage networks and floodplains. A case study implementation in a mid-sized coastal city demonstrates the framework's efficacy, showing significant improvements in flood forecasting accuracy (reducing error by 23% compared to traditional models) and decision response times (average reduction of 40%). Key components include real-time data ingestion from IoT sensors, automated calibration using ensemble Kalman filtering, and a multi-criteria decision analysis module for evaluating intervention strategies. The results highlight the potential of digital twins to transform urban flood management from reactive to proactive, supporting resilience planning and emergency response. Challenges related to data integration, computational demands, and stakeholder adoption are discussed. This research contributes to the growing body of knowledge on urban informatics and cyber-physical systems, providing a scalable blueprint for cities worldwide.