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

TL;DR

This paper introduces a reinforcement learning framework for long-term flood adaptation planning in urban transportation, effectively balancing costs and impacts under climate uncertainty, demonstrated through a Copenhagen case study.

Contribution

It presents a novel RL-based decision-support model integrating climate projections, flood modeling, and transport simulation for adaptive infrastructure planning.

Findings

RL framework outperforms traditional methods in discovering adaptive strategies

The model identifies coordinated spatial and temporal adaptation pathways

Reinforcement learning effectively balances investment costs and impact reduction

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.