Modeling and Simulating Adaptation Strategies Against Sea-Level Rise Using Multiagent Deep Reinforcement Learning
Bibliographic Data
| ID | 22108352 |
|---|---|
| Authors | Salman Sadiq Shuvo (0000-0003-1577-7341, University of South Florida), Yasin Yilmaz (0000-0003-2014-3060, University of South Florida), Alan Bush (0000-0002-0407-5750, Honors College, University of South Florida, Tampa, FL, USA), Alan J Bush (University of South Florida), Mark Hafen (University of South Florida) |
| Year | 2022 |
| Volume | 9 |
| Issue | 4 |
| Pages | 1185-1196 |
| Publication date | 2022-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2021.3122282 |
| OpenAlex | W3209177686 |
| Language | EN |
| Citations received | 3 |
| References cited | 24 |
Sea-level rise (SLR) problem, which is a major outcome of climate change, has been well documented and studied. Although it is globally observed due to climate change, local projections are needed to plan SLR adaptation strategies accurately. Since SLR is a community-wide multistakeholder problem at the local level, adaptation strategies can be more successful if the main stakeholders, e.g., government, residents, and businesses, collaborate in shaping them. Simulating the local socioeconomic system around SLR, including the interactions between essential stakeholders and nature, can be an effective way of evaluating different adaptation strategies and planning the best strategy for the local community. This work presents how such an SLR socioeconomic system can be modeled as a Markov decision process (MDP) and simulated using multiagent reinforcement learning (RL). The proposed multiagent RL framework serves two purposes. It provides a general scenario planning tool to investigate the cost–benefit analysis of natural events (e.g., flooding and hurricane) and agents’ investments (e.g., infrastructure improvement). It also shows how much the total cost due to SLR can be reduced over time by optimizing the adaptation strategies. We demonstrate the proposed scenario planning tool using available economic data and sea-level projections for Pinellas County, Florida, in a case study
Climate change · Climate change adaptation · Environmental planning · Environmental resource management · Geography · Markov decision process · Markov process · Multi-agent system · Operations research · Process management · Reinforcement learning · Coastal and Marine Dynamics · Computer Science · Engineering · Environmental Science · Flood Risk Assessment and Management · Tropical and Extratropical Cyclones Research · Artificial Intelligence
| Citations per year | 1.5 |
|---|---|
| Citation span | 2024 - 2026 (3) |
| Highly cited | No |