Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

• URL: http://arxiv.org/abs/2511.03238v1
• Date: Wed, 05 Nov 2025 07:00:55 GMT
• Title: Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning
• Authors: Miguel Costa, Arthur Vandervoort, Martin Drews, Karyn Morrissey, Francisco C. Pereira,
• Abstract summary: Reinforcement Learning (RL) holds significant promise in tackling complex, dynamic, and uncertain problems.<n>We use RL to identify which climate adaptation pathways lead to a higher Quality of Life in the long term.<n>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.
• Score: 2.1499528348377535
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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