Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen

Miguel Costa, Morten W. Petersen, Arthur Vandervoort, Martin Drews, Karyn Morrissey +1 more
9/27/2024
cs.LG

Official code: MLSM-at-DTU/floods_transport_rl (2 stars, MIT, last commit 2025-09-18).

Abstract

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}.

View on arXivView PDF

Code Implementations(1)

MLSM-at-DTU/floods_transport_rlOfficial100%
MIT

Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen

20Python, Jupyter NotebookNov 29, 20241 years agoMIT

Similar Papers

Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport

95% match2026

Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning

94% match2026

Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making

90% match2025

RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models

84% match1 repo82024

Reinforcement Learning (RL) Meets Urban Climate Modeling: Investigating the Efficacy and Impacts of RL-Based HVAC Control

82% match2025

Urban Spatio-Temporal Foundation Models for Climate-Resilient Housing: Scaling Diffusion Transformers for Disaster Risk Prediction

81% match2026

Cite this paper

@article{costa2024climate,
  title  = {Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen},
  author = {Miguel Costa and Morten W. Petersen and Arthur Vandervoort and Martin Drews and Karyn Morrissey and Francisco C. Pereira},
  year   = {2024},
  eprint = {2409.18574},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.18574v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2409.18574.svg)](https://codeofpaper.com/paper/2409.18574)

Discussion