1,1,2,- 1Smart Infrastructure and Green Technologies Research Group, School of Computing and Engineering, University of West London, St Mary's Rd, London, W5 5RF, UK (saeid.najjarghbel@uwl.ac.uk)
- 2Centre for Engineering Research, School of Physics, Engineering, Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK
Real-time flood risk assessment requires integrated frameworks that not only forecast flood dynamics accurately [1] but also capture how people respond to rapidly changing hazard conditions [2,3]. This study develops a novel real-time flood impact assessment framework that couples AI-driven flood forecasting (Long Short-Term Memory) with an agent-based model (ABM) to evaluate human mobility disruption and behavioural adaptation during flood events.
The outputs of the flood forecasting model are dynamically transferred to an ABM that represents urban populations, daily activity schedules, and transport networks. Agent (i.e., road users with different demographic attributes) decision-making is governed by a novel risk priority index (RPI), which integrates direct flood exposure, official communication, and demographic vulnerability of agents. Watts-Strogatz small-world network was used to consider the interaction of agents and realistically represent information diffusion, allowing the dissemination of risk awareness.
Results reveal substantial real-time impacts on urban mobility, with significant increases in travel times, particularly during peak hours. Moreover, incorporating behavioural adaptation through the RPI in agent-based modelling highlights the critical role of flood-risk information sharing among. A balanced combination of personal flood experience (Individual RPI) and socially shared information (average RPI of neighbouring agents) leads to faster and more effective formation of risk awareness across the population. The proposed AI-driven, agent-based framework enables real-time evaluation of flood impacts on population groups and transport systems, offering a powerful tool for emergency response planning and operational flood risk mitigation by local authorities.
References
[1] Piadeh F., Bakhtiari, V., Piadeh, F. (2026). Automated novel real-time framework for rainfall data imputation in flood early warning systems, Engineering Applications of Artificial Intelligence, 1164(B), p.113348. https://doi.org/10.1016/j.engappai.2025.113348
[2] Qin, H., Liang, Q., Chen, H., De Silva, V. (2024). A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation. Journal of Hydrology, 643, 131918. https://doi.org/10.1016/j.jhydrol.2024.131918
[3] 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
How to cite: Najjar-Ghabel, S., Behzadian, K., Piadeh, F., and Ardakanian, A.: Real-Time Flood Risk Assessment Using Coupled Agent-Based and AI-Driven Flood Forecasting Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14951, https://doi.org/10.5194/egusphere-egu26-14951, 2026.
