2152000
Project Grant
Overview
Grant Description
Sbir Phase I: A digital platform to assess water quality at urban-watershed interfaces - The broader impact of this Sbir Phase I project is to help manage water quality at the boundaries of cities and watersheds.
The proposed work develops a financial system to help cities and public utilities build infrastructure projects. This system integrates physical data with artificial intelligence and advanced monitoring systems. It will serve as a scalable analytics platform using environmental, economic, and social data for financing projects in water quality management.
The proposed project develops analytics and a financial instrument for environmental adaptation and water restoration projects. It uses open-source data management, sensors and interfaces, and mathematical models in a system with state-of-the-art artificial intelligence-enabled digital twin technology.
The environmental data will be used in an integrated watershed model using principles of uncertainty analysis and neural network-based learning. The econometric model combines uncertainty analysis with reinforcement learning where accuracy in prognostics is incentivized.
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.
The proposed work develops a financial system to help cities and public utilities build infrastructure projects. This system integrates physical data with artificial intelligence and advanced monitoring systems. It will serve as a scalable analytics platform using environmental, economic, and social data for financing projects in water quality management.
The proposed project develops analytics and a financial instrument for environmental adaptation and water restoration projects. It uses open-source data management, sensors and interfaces, and mathematical models in a system with state-of-the-art artificial intelligence-enabled digital twin technology.
The environmental data will be used in an integrated watershed model using principles of uncertainty analysis and neural network-based learning. The econometric model combines uncertainty analysis with reinforcement learning where accuracy in prognostics is incentivized.
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.
Grant Program (CFDA)
Awarding Agency
Place of Performance
Herndon,
Virginia
20171-2716
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Resbonds International Corporation was awarded
Project Grant 2152000
worth $254,976
from Directorate for Technology, Innovation and Partnerships in April 2022 with work to be completed primarily in Herndon Virginia United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.041 Engineering.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:A Digital Platform to Assess Water Quality at Urban-Watershed Interfaces
Abstract
The broader impact of this SBIR Phase I project is to help manage water quality at the boundaries of cities and watersheds.The proposed work develops a financial system to help cities and public utilities build infrastructure projects. This system integrates physical data with artificial intelligence and advanced monitoring systems. It will serve as a scalable analytics platform using environmental, economic, and social data for financing projects in water quality management.The proposed project develops analytics and a financial instrument for environmental adaptation and water restoration projects. It uses open-source data management, sensors and interfaces, and mathematical models in a system with state-of-the-art artificial intelligence-enabled digital twin technology. The environmental data will be used in an integrated watershed model using principles of uncertainty analysis and neural network-based learning.The econometric model combines uncertainty analysis with reinforcement learning where accuracy in prognostics is incentivized.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.
Topic Code
ET
Solicitation Number
NSF 21-562
Status
(Complete)
Last Modified 4/21/22
Period of Performance
4/15/22
Start Date
3/31/23
End Date
Funding Split
$255.0K
Federal Obligation
$0.0
Non-Federal Obligation
$255.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2152000
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
490707 DIVISION OF INDUSTRIAL INNOVATION
Awardee UEI
H3YAUAAPJAA7
Awardee CAGE
94ZA6
Performance District
10
Senators
Mark Warner
Timothy Kaine
Timothy Kaine
Representative
Jennifer Wexton
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $254,976 | 100% |
Modified: 4/21/22