Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
This addresses climate adaptation planning for policymakers by integrating quality of life into decision-making, though it appears incremental as it applies existing RL methods to a new domain.
The paper tackles urban flooding from climate change by using reinforcement learning to identify adaptation pathways that improve long-term quality of life, with preliminary results showing it outperforms other planning strategies.
Urban flooding is expected to increase in frequency and severity as a consequence of climate change, causing wide-ranging impacts that include a decrease in urban Quality of Life (QoL). Meanwhile, policymakers must devise adaptation strategies that can cope with the uncertain nature of climate change and the complex and dynamic nature of urban flooding. Reinforcement Learning (RL) holds significant promise in tackling such complex, dynamic, and uncertain problems. Because of this, we use RL to identify which climate adaptation pathways lead to a higher QoL in the long term. We do this using an Integrated Assessment Model (IAM) which combines a rainfall projection model, a flood model, a transport accessibility model, and a quality of life index. Our preliminary results suggest that this approach can be used to learn optimal adaptation measures and it outperforms other realistic and real-world planning strategies. Our framework is publicly available: https://github.com/MLSM-at-DTU/maat_qol_framework.