Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

TL;DR

This paper presents a reinforcement learning framework that integrates multiple models to optimize climate adaptation strategies aimed at improving urban quality of life amid increasing flood risks due to climate change.

Contribution

It introduces a novel RL-based approach that combines diverse models to identify effective long-term climate adaptation pathways for urban QoL improvement.

Findings

RL outperforms traditional planning strategies in simulations

The framework effectively learns optimal adaptation measures

Preliminary results show promising potential for real-world application

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.