A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".