An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper formalizes the Water and AI Feedback Loop, introduces the Water Consumption Impact index, and shows water burden from AI data centers varies from 0.2% to 134% of local capacity across ten US sites.
citing papers explorer
-
Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI
An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.
-
AI Data Centers and the Water Use Feedback Loop
The paper formalizes the Water and AI Feedback Loop, introduces the Water Consumption Impact index, and shows water burden from AI data centers varies from 0.2% to 134% of local capacity across ten US sites.