Flood Management Strategies Using Agent-Based Modelling and Public APIs: A Case Study in the UK

Flood management is a critical challenge, especially in areas where climate change and urbanisation have altered the level of risk from floods [1]. The conventional approach to flood risk assessment is usually insensitive to behavioural couplings between human behaviour and flood dynamics, which severely affect the result of any management strategy [2]. On the other hand, behavioural simulation models such as agent-based modelling (ABM), present a promised alternative by allowing bottom-up explorations of flood management scenarios [3]. The present study tries to fill this gap by proposing activity-based ABM devised to evaluate flood management strategies for different flood risk scenarios in a UK case study. The model uses real-time travel data from the Google Maps application program interface (API) intending to simulate individual behavioural dynamics realistically in terms of movement patterns and responses in case of flooding. By integrating these behavioural insights with flood risk maps and infrastructural data, the model assesses the effectiveness of interventions such as flood warnings, evacuation plans, and adaptive infrastructure. The findings of this research demonstrate how ABMs can be used to inform decision-makers, contributing to the improvement of both short-term flood preparedness and response as well as long-term planning for infrastructure development. This study illustrates how dynamic and realistic modelling of interactions between humans and their environment can reveal the role of ABMs in advancing flood resilience planning.