Coupled Risk-aware Agent-Based Framework and Hydrodynamic Modelling for Urban Flood Impact Assessment

Agent-based modelling (ABM) is increasingly recognised as an essential tool for urban flood risk assessment to analyse the response of people and transport systems under dynamically evolving flood conditions [1,2]. However, the dynamic evolution of risk awareness and the exchange of risk-related information during flood events remain inadequately represented in many existing modelling approaches [3, 4]. This study presents an integrated flood-impact assessment framework that couples a hydrodynamic flood model with a risk-based ABM to evaluate the impact of flooding on travelling population groups and road network performance.

Flood modelling is simulated using a hydrodynamic model calibrated through the sequential uncertainty-fitting algorithm, providing reliable spatio-temporal flood characteristics. These hydraulic outputs are dynamically linked to an ABM representing urban populations with realistic daily activity. People’s behavioural adaptation is governed by a novel risk priority index, which evolves based on direct flood exposure, institutional communication, and risk-information exchange. People are assumed to interact together through Watts-Strogatz small-world network, enabling realistic diffusion of risk awareness across the population.

Results show that flood-induced road closures trigger sharp increases in travel times. Agent-based analysis revealed that, among population groups, adults experienced the highest total flood exposure, followed by seniors and children. Moreover, travel mode strongly influences vulnerability, with cycling users experiencing the highest exposure levels, followed by public transit, walking, and driving users. The proposed framework provides a robust decision-support tool for evaluating how risk awareness and social interaction through an agent-based model influence road users and road network performance.

References

[1] Bakhtiari, V., Piadeh, F., Chen, A. S., & Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, 121426. https://doi.org/10.1016/j.eswa.2023.121426

[2] Najjar Ghabel, S.,  Zarghami, M., Akhbari, M., & Nadiri A.A.  (2019). Groundwater Management in Ardabil Plain Using Agent-Based Modeling, Iran-Water Resources Research 15, 1–16.

[3] Kunreuther, H., & Pauly, M. (2006). Rules rather than discretion: Lessons from Hurricane Katrina. Journal of Risk and Uncertainty, 33(1–2), 101–116. https://doi.org/10.1007/s11166-006-0173-x

[4] Lo, A. Y. (2013). The role of social norms in climate adaptation: Mediating risk perception and flood insurance purchase. Global Environmental Change, 23(5), 1249–1257. https://doi.org/10.1016/j.gloenvcha.2013.07.019