Agent-Based Dynamic Assessment of Pedestrian Flood Risk Considering Age and Education

Urban floods pose an escalating threat to sustainable urban development, particularly in densely populated regions where demographic heterogeneity and differential responses to warnings shape flood vulnerability. However, the existing flood risk assessments of humans often neglect age-specific susceptibility and education-dependent behavioral responses. This study develops an agent-based flood risk model (AFRM) that couples the agent model with a hydrodynamic model to support sustainable urban flood management. Human agents are characterized by spatial distributions derived from population heatmap data, daily mobility patterns governed by a probabilistic finite-state machine, age-differentiated vulnerability thresholds (minors, adults, and seniors), and education-dependent probabilities of responding to warnings. The AFRM is established by integrating human agents and the urban flood process based on the NetLogo platform. Subsequently, the study assesses the dynamic human flood risk under various return periods, and explores the effects of age structures and early warning on urban flood risk assessment. Taking the Huayuan Road area of Zhengzhou City, China as a case study, the results demonstrate the following: (1) the AFRM achieves reasonable accuracy, with 7.17% mean absolute percentage errors across all risk levels; (2) neglecting age structures underestimates the flood risk assessment by 18% to 29%, as minors and seniors exhibit a higher susceptibility to flooding; and (3) early warnings reduced the medium- and high-risk exposure by 45% to 58%, highlighting the potential of warning lead time strategies to enhance urban flood resilience. This research provides the support for urban flood safety and sustainable development in a changing climate.