AI & Environment Resource HubSingh et al. · 2024-12-15
Transformer models outperform LSTMs in streamflow prediction, improving hydrological forecasting for water resource management and climate adaptation.
Singh et al.. 2024-12-15. Temporal Sequence Transformer to Advance Long-Term Streamflow Prediction. https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2024/5/paper.pdf (AI & Environment Resource Hub; record paper-032; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 36 · Original ID: paper-032.
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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
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.