A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

Abstract

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.