A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran

Digital twins, virtual representations of physical systems, integrate sensor data and predictive models to enable real-time simulation and analysis. They are instrumental in monitoring weather, infrastructure health, and water levels, particularly in flood management. By modeling mitigation techniques, forecasting risks, and enhancing emergency responses, digital twins improve decision-making, reduce economic losses, and enhance public safety in flood-prone areas [1][2]. This study developed a digital twin system to monitor and forecast flood disasters in western Iran. The system combined multidimensional sensor data on temperature, flood flow, vegetation cover, and water levels using an offline databank. Time-series analysis tracked trends, while a linear regression-based predictive model estimated future flood conditions. Threshold values for flood warnings and high-risk alerts were defined using hydrological principles and environmental data [3]. Game theory concepts were employed to optimize flood management strategies by modeling interactions among stakeholders, including authorities, responders, and communities. A non-cooperative game theory approach simulated conflicting objectives, such as minimizing economic losses and optimizing resource allocation. Stable solutions were identified through the Nash equilibrium, ensuring no stakeholder could unilaterally improve outcomes. Visualization dashboards presented time-series data, risk levels, and stakeholder strategies, facilitating informed decision-making. Simulation results demonstrated the system's effectiveness in flood risk assessment. Water levels remained below the 2.5-meter warning threshold but rose significantly during simulated abnormal conditions. In later stages, some areas approached the 3.0-meter high-risk threshold, indicating zones vulnerable to flooding. Flood flow rates frequently exceeded the 40 m³/s threshold, with peaks above 60 m³/s, highlighting the need for continuous flow monitoring. Temperature fluctuations were minimal, consistently below the 25°C threshold, suggesting limited influence on flood risks during the study. However, vegetation cover often fell below the 30% threshold, correlating with increased flood risks and reinforcing its importance in mitigation. The system effectively categorized risk levels, with most instances classified as "Normal" or "Warning." High-risk alerts were concentrated during elevated water levels and flows. This research highlights the potential of digital twins for real-time flood monitoring and collaborative decision-making, providing a robust framework to enhance disaster resilience.

Keywords: Digital Twin; Flood Risk Assessment; Game Theory; Predictive Modeling; Multidimensional Data Analysis.

References

[1] Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2021, May). Digital twin: a city-scale flood imitation framework. Canadian Society of Civil Engineering Annual Conference (pp. 577–588). Singapore: Springer Nature Singapore.

[2] Gheibi, M., & Moezzi, R. (2023). A Social-Based Decision Support System for Flood Damage Risk Reduction in European Smart Cities. Quanta Research, 1(2), 27–33.

[3] Kreps, D. M. (1989). Nash equilibrium. In Game Theory (pp. 167–177). London: Palgrave Macmillan UK.