Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

TL;DR

A novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning that 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.

Abstract

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.

Figures (5)

• Figure 1: Integrated Assessment Model using reinforcement learning to find the best sequence of transport-related adaptation policies for rainfall events in Copenhagen from 2024--2100.
• Figure 2: Comparison of different adaptation strategies. Total reward (top-left, scaled by $10^8$), and reward components are shown: infrastructure damage costs (top-middle), travel delays (top-right), travel cancellations (bottom-left), actions direct costs (bottom-middle), and action maintenance costs (bottom-right). All values correspond to Danish Krone (DKK). Please note the different y-axis scale across figures.
• Figure 3: Average (and standard deviations shaded) for all five reward components over the period 2024--2100 under the RCP4.5 climate scenario. While Random Control reduces impacts through high and persistent investment costs, the RL policy achieves sustained impact reduction with substantially lower and more stable adaptation expenditures. All values correspond to Danish Krone (DKK). Please note the different y-axis scale across subfigures.
• Figure 4: Adaptation measures taken over time per zone in Copenhagen's city center for one episode (run) for a RCP4.5 scenario.
• Figure 5: Density of actions used over time for 10 runs for a RCP4.5 scenario. Bioretention Planters are shown in top-left, Soakaways in top-right, Storage Tanks in bottom-left, and Porous Asphalt in bottom-right.