Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
arXiv:2603.06278v1 Announce Type: new 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
arXiv:2603.06278v1 Announce Type: new 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.
Executive Summary
This article proposes a novel decision-support framework using reinforcement learning (RL) for long-term flood adaptation planning in urban transportation systems. The framework combines rainfall projection, flood modeling, transport simulation, and impact quantification to learn adaptive strategies that balance investment and maintenance costs against avoided impacts. A case study of Copenhagen's inner city demonstrates the framework's potential in discovering coordinated spatial and temporal adaptation pathways, outperforming traditional optimization approaches. The results showcase the potential of RL as a flexible decision-support tool for adaptive infrastructure planning under climate uncertainty.
Key Points
- ▸ Reinforcement learning for climate change-resilient transport
- ▸ Integrated assessment model combining rainfall projection, flood modeling, and transport simulation
- ▸ Case study of Copenhagen's inner city demonstrating the framework's effectiveness
Merits
Flexibility and Adaptability
The RL-based approach allows for adaptive strategies that can respond to changing climate conditions and uncertainty
Comprehensive Integration
The framework integrates multiple components, including rainfall projection, flood modeling, and transport simulation, to provide a holistic approach to flood adaptation planning
Demerits
Complexity and Data Requirements
The framework requires significant data and computational resources, which may be a limitation for smaller cities or regions with limited resources
Uncertainty and Robustness
The framework's performance may be affected by uncertainty in climate projections and other inputs, which may impact its robustness and reliability
Expert Commentary
The article presents a significant contribution to the field of climate change adaptation and infrastructure planning. The use of reinforcement learning to develop adaptive strategies for flood resilience is a novel and promising approach. The framework's ability to integrate multiple components and learn from experience makes it a valuable tool for decision-support. However, further research is needed to address the limitations and uncertainties associated with the framework, including data requirements and robustness. Nevertheless, the article demonstrates the potential of RL as a flexible and effective decision-support tool for adaptive infrastructure planning under climate uncertainty.
Recommendations
- ✓ Further research is needed to refine the framework and address its limitations, including data requirements and uncertainty
- ✓ The framework should be applied to other cities and regions to test its scalability and effectiveness in different contexts