AI & Environment Resource HubKairui Feng; Ning Lin; Robert E. Kopp; Siyuan Xian; Michael Oppenheimer · 2025-03-18
This study demonstrates that reinforcement learning (RL) can enhance climate adaptation decision-making by dynamically optimizing coastal flood risk mitigation strategies for Manhattan, NYC, significantly reducing expected costs and improving resilience to uncertainties compared to conventional methods.
Kairui Feng; Ning Lin; Robert E. Kopp; Siyuan Xian; Michael Oppenheimer. 2025-03-18. Reinforcement Learning–Based Adaptive Strategies for Climate Change Adaptation: An Application for Coastal Flood Risk Management. https://www.pnas.org/doi/10.1073/pnas.2402826122 (AI & Environment Resource Hub; record paper-160; collection snapshot 2026-09-15).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 164 · Original ID: paper-160.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Severe flash floods in Charikar, Afghanistan, on August 26, 2020, were driven by extreme atmospheric instability, deep low-level convergence, and local topography, with climate change intensifying frontal activity and baroclinicity in the region.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Analysis of rainfall extremes in Côte d'Ivoire's watersheds (1976–2050) reveals decreasing drought indices in historical data but increasing flood-related indices under RCP 4.5 and RCP 8.5 scenarios, highlighting growing climate-induced disaster risks.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Machine learning models predict hydrological changes in the 3S River Basin under climate change scenarios, showing increased flood risk in some areas and reduced streamflow in others.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
Future rainfall data in Korea may underestimate multi-year droughts, highlighting limitations for evaluating long-term water supply resilience, particularly in the Boryeong Dam basin.