How can AI-driven models and decision support help address water scarcity challenges of the 21st century?
Can AI-enabled approaches and modeling lead to better stewardship and planning of water resources?
To answer these questions, the AgAID team is working on designing AI and science-guided machine learning frameworks at the human-water-climate nexus; facilitating an information shift from water supply to spatio-temporal availability. These frameworks will help irrigation managers and water districts to make more effective seasonal and long-horizon planning, forecasting, and allocation decisions.
The research thrust covers multiple use-cases associated with water allocation decisions at multiple spatial (farm, district, watershed) and temporal (weekly, seasonal, long-term) scales.
Example use-cases under this thrust include:
- Irrigation scheduling;
- Water leasing or trading; and
- Storage investments.