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Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

Miguel Costa, Arthur Vandervoort, Martin Drews, Karyn Morrissey, Francisco C. Pereira

TL;DR

The paper addresses maintaining urban Quality of Life (QoL) amid increasing urban flooding by learning optimal climate adaptation pathways. It introduces an Integrated Assessment Model (IAM) that combines rainfall projection, flood modelling, transport accessibility, and a QoL index, and applies reinforcement learning to identify policy sequences that maximize QoL under climate uncertainty. Preliminary Copenhagen case studies show the RL-based policies outperform baselines, demonstrating the framework's potential and sensitivity to QoL weighting. The authors provide a publicly available framework for extending QoL-oriented climate adaptation to other cities and hazards.

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.

Incorporating Quality of Life in Climate Adaptation Planning via Reinforcement Learning

TL;DR

The paper addresses maintaining urban Quality of Life (QoL) amid increasing urban flooding by learning optimal climate adaptation pathways. It introduces an Integrated Assessment Model (IAM) that combines rainfall projection, flood modelling, transport accessibility, and a QoL index, and applies reinforcement learning to identify policy sequences that maximize QoL under climate uncertainty. Preliminary Copenhagen case studies show the RL-based policies outperform baselines, demonstrating the framework's potential and sensitivity to QoL weighting. The authors provide a publicly available framework for extending QoL-oriented climate adaptation to other cities and hazards.

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.

Paper Structure

This paper contains 7 sections, 1 equation, 2 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Rainfall Projection and Flood Model
  4. Transport Accessibility and Quality of Life Index
  5. Reinforcement Learning
  6. Preliminary Results and Discussion
  7. Appendix A

Figures (2)

  • Figure 1: IAM using RL to learn what the best sequence of adaptation policies are to maximise quality of life under climate changing scenarios.
  • Figure 2: Top row: Side-by-side comparison of No Control and Learnt Policy result by city zone. Bottom row: Comparison of total reward between Learnt Policy and baselines (left), and between reward components (right).