AI & Environment Resource HubQuyen Nguyen; Sangam Shrestha; Suwas Ghimire; S. Mohana Sundaram; Wenchao Xue; Salvatore G. P. Virdis; Manisha Maharjan · 2023-08-01
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.
Quyen Nguyen; Sangam Shrestha; Suwas Ghimire; S. Mohana Sundaram; Wenchao Xue; Salvatore G. P. Virdis; Manisha Maharjan. 2023-08-01. Application Of Machine Learning Models In Assessing The Hydrological Changes Under Climate Change In The Transboundary 3S River Basin. https://iwaponline.com/jwcc/article/14/8/2902/96771/Application-of-machine-learning-models-in (AI & Environment Resource Hub; record paper-098; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 102 · Original ID: paper-098.
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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
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.
Shared topics: Climate and greenhouse gases; Water; Oceans and coasts; Weather, hazards, and adaptation
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.