Abstract & Details
Description
Award ID: 2134892
Water is the driving force behind extreme events like floods, droughts and wildfires. These events have cost the US $234.3B in damages just in the past three years, and this figure is projected to increase. Recent events like the record setting wildfires in California and the mega drought on the Colorado river are merely the latest illustrations. Historical data are no longer a reliable guide for the risks we will face in the future. This project addresses the uncertainty that poses a huge challenge for decision makers. HydroGEN is a web-based machine learning (ML) platform that generates custom hydrologic scenarios on demand. It combines powerful physics-based simulations with ML and observations to provide customizable scenarios from the bedrock through the treetops. Without any prior modeling experience, water managers and planners can directly manipulate state-of-the-art tools to explore scenarios that matter to them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Michael Reksulak
Water is the driving force behind extreme events like floods, droughts and wildfires. These events have cost the US $234.3B in damages just in the past three years, and this figure is projected to increase. Recent events like the record setting wildfires in California and the mega drought on the Colorado river are merely the latest illustrations. Historical data are no longer a reliable guide for the risks we will face in the future. This project addresses the uncertainty that poses a huge challenge for decision makers. HydroGEN is a web-based machine learning (ML) platform that generates custom hydrologic scenarios on demand. It combines powerful physics-based simulations with ML and observations to provide customizable scenarios from the bedrock through the treetops. Without any prior modeling experience, water managers and planners can directly manipulate state-of-the-art tools to explore scenarios that matter to them. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
NSF Program Director: Michael Reksulak
| Status | Active |
|---|---|
| Effective start/end date | 10/01/21 → 03/31/27 |
Lead and Sub-Awardee Organization(s)
Funding
- Other Programs (Technology): $5,000,000.00
Active Fiscal Year
- FY2024
- FY2023
- FY2022
- FY2027
- FY2026
- FY2025
Start Fiscal Year
- FY2022
TIP Programs
- Other Programs (Technology)
Key Technology Areas
- Artificial Intelligence
- (confidence score: 100%)
- Data and Cybersecurity
- (confidence score: 96%)
- Disaster Prevention and Mitigation
- (confidence score: 90%)
Technology Foci
- Natural disaster prevention and mitigation
- (confidence score: 97%)
- Data Management / Databases
- (confidence score: 97%)
- Machine Learning Training Data
- (confidence score: 99%)
- Machine Learning (ML)
- (confidence score: 100%)
Congressional District at Award
- District n. 07 of Arizona
Current Congressional District
- District n. 07 of Arizona
United States
- Arizona
Core Based Statistical Area (CBSA)
- Tucson, AZ
County
- County: Pima, AZ
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint. Learn more about Elsevier's Fingerprint Engine here: https://beta.elsevier.com/products/elsevier-fingerprint-engine