用强化学习优化哥本哈根防洪与交通应对策略
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen
- 用强化学习自动规划防洪措施的实施时机和位置
- 可降低洪水对交通与基础设施的直接和间接影响
- 适合城市规划与气候适应政策制定者参考
由于气候变化，许多地区极端降雨事件的频率和强度预计将进一步上升，导致城市内涝风险加剧。这些洪水可能损坏交通基础设施并扰乱出行，凸显城市适应日益增长风险的紧迫性。强化学习（RL）是一种强大的工具，可用于发现最优适应策略，在不确定性下确定适应措施的部署方式与地点。本研究利用强化学习识别最有效的干预时机与区域，旨在减少洪水带来的直接与间接影响。框架整合了未来降雨与洪水的气候变迁预测，模拟全市机动车出行，并量化对基础设施与出行的影响。初步结果显示，该基于强化学习的方法能显著提升决策质量，优先在特定城区实施干预，并确定最佳实施时段。相关框架已公开：\url{https://github.com/MLSM-at-DTU/floods_transport_rl}。
原文摘要 · Abstract (English)
Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \url{https://github.com/MLSM-at-DTU/floods_transport_rl}.
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