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

2026-03-06

AI Summary

This study addresses the persistent disruptions to urban transportation systems caused by climate change–induced heavy rainfall and pluvial flooding by proposing a reinforcement learning–based decision support framework. Integrating climate scenario analysis, flood modeling, traffic simulation, and impact assessment, the framework optimizes dynamic adaptive investment strategies under deep uncertainty. It represents the first application of reinforcement learning to climate adaptation planning for urban transport, enabling the generation of spatiotemporally coordinated adaptive investment pathways that dynamically balance cost and benefit trade-offs. Applied to a Copenhagen case study over the period 2024–2100, the approach outperforms conventional optimization methods, significantly enhancing system resilience while effectively balancing investment costs against avoided disaster losses.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• climate adaptation
• urban transportation
• pluvial flooding
• infrastructure planning
• climate uncertainty

Innovation

• Reinforcement Learning
• Integrated Assessment Model
• Climate Adaptation
• Flood Resilience
• Adaptive Infrastructure Planning