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

TL;DR

This paper presents a reinforcement learning framework to optimize urban flood adaptation strategies in Copenhagen, integrating climate projections and mobility models to improve decision-making and reduce flood impacts.

Contribution

It introduces a novel RL-based approach that combines climate data and city-wide mobility modeling for effective flood adaptation planning.

Findings

RL approach can prioritize interventions effectively

Framework reduces flood impact on infrastructure and mobility

Preliminary results show improved decision-making

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}.