Summary of Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen, by Miguel Costa et al.
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen
by Miguel Costa, Morten W. Petersen, Arthur Vandervoort, Martin Drews, Karyn Morrissey, Francisco C. Pereira
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper applies reinforcement learning (RL) to develop optimal strategies for urban flooding adaptation in cities. The authors leverage climate change projections and city-wide motorized trip models to identify the most effective timing and locations for implementing measures to reduce both direct and indirect impacts of flooding on infrastructure and mobility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses RL to find the best ways to adapt to urban flooding, which is expected to increase due to climate change. The goal is to reduce damage to transport infrastructure and disruptions to mobility. The authors integrate climate projections with city trip models to prioritize interventions in specific areas and identify optimal implementation times. |
Keywords
* Artificial intelligence * Reinforcement learning
