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High-probability grants
According to our matching algorithm, Peter Melchior is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2020 — 2021 |
Merchant, Nirav (co-PI) [⬀] O'leary, Patrick Maxwell, Reed Condon, Laura [⬀] Melchior, Peter |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nsf Convergence Accelerator - Track D: Hidden Water and Hydrologic Extremes: a Groundwater Data Platform For Machine Learning and Water Management
The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to utilize artificial intelligence methods such as machine learning (ML) to achieve better water management outcomes that directly benefit society by developing the ability to better plan for and manage extreme events through improved hydrologic forecasting. HydroFrame-ML is motivated by, and structured around, applied solutions for water management planning and decision making. Extreme events like drought and floods have far-reaching societal impacts. They are common, costly and likely to get worse in the future. The project team is partnered with the Bureau of Reclamation, which is the largest wholesale water provider in the country, providing water to more than 31 million people and 10 million acres of farmland. The Bureau of Reclamation will drive use case design and the metrics used to evaluate success in Phase 1, as well as partner in the expansion of the project team for Phase 2. Additionally, the project team will develop hands-on activities and challenges designed to give undergraduates experience in machine learning and data science, in the context of pressing real-world challenges. Aided by the planned addition of a STEM mentorship program partner in Phase 2, the team will build content with the vision of helping to broaden participation of underrepresented students well beyond the timeframe of this project.
The proposed project brings together the most physically rigorous national scale groundwater simulations developed through HydroFrame with national leaders in Earth Systems Modeling and water management. By providing end-to-end workflows combining state of groundwater science with operational management tools, HydroFrame-ML will advance both large-scale water management as well as our understanding of how human operations and groundwater interact in extreme events. Their products will provide innovative ways to improve forecasts and in the process will expand our knowledge about the (1) contributions of groundwater to extreme events in managed systems; (2) biases in our current risk-assessment approaches which do not consider groundwater; and (3) potential to improve long-term sustainability by more actively managing groundwater and accounting for groundwater surface water interactions in projections.
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.
|
0.948 |
2021 — 2023 |
Merchant, Nirav (co-PI) [⬀] Condon, Laura [⬀] Melchior, Peter Maxwell, Reed |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Track D: Hidden Water and Extreme Events: Hydrogen, a Physically Rigorous Machine Learning Platform For Hydrologic Scenario Generation
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.
|
0.948 |