Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
This work provides a decision-support tool for urban planners and policymakers to develop climate change-resilient transport infrastructure, addressing the long-term, sequential nature of infrastructure investments under deep climate uncertainty.
This paper addresses the challenge of designing climate change adaptation strategies for urban transportation systems facing increased pluvial flooding. The authors propose a reinforcement learning framework that learns adaptive strategies to balance investment and maintenance costs against avoided impacts, demonstrating its ability to discover coordinated spatial and temporal adaptation pathways and yield more resilient strategies compared to traditional optimization approaches in a Copenhagen case study.
Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature of infrastructure investments, deep climate uncertainty, and the complex interactions between flooding, infrastructure, and mobility impacts. In this work, we propose a novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning. Formulated as an integrated assessment model (IAM), the framework combines rainfall projection and flood modeling, transport simulation, and quantification of direct and indirect impacts on infrastructure and mobility. Our RL-based approach learns adaptive strategies that balance investment and maintenance costs against avoided impacts. We evaluate the framework through a case study of Copenhagen's inner city over the 2024-2100 period, testing multiple adaptation options, and different belief and realized climate scenarios. Results show that the framework outperforms traditional optimization approaches by discovering coordinated spatial and temporal adaptation pathways and learning trade-offs between impact reduction and adaptation investment, yielding more resilient strategies. Overall, our results showcase the potential of reinforcement learning as a flexible decision-support tool for adaptive infrastructure planning under climate uncertainty.