The U.S. Geological Survey (USGS) is offering a funding opportunity for research focused on advancing stream flow estimation. This grant is specifically for a Cooperative Ecosystem Studies Unit (CESU) partner to develop and refine Artificial Intelligence (AI) models. The core purpose involves creating deep convolutional neural networks and establishing baseline AI models to enhance the performance of flow estimation. Leveraging the expertise of Research Scientists at the EESC in ongoing AI-based stream flow studies, this initiative aims to bridge computer science methodologies with ecological science. The objective is to significantly improve our understanding and predictive capabilities for freshwater stream flow through innovative AI applications.
Opportunity ID: 339488
General Information
| Document Type: | Grants Notice |
| Funding Opportunity Number: | G22AS00314 |
| Funding Opportunity Title: | Cooperative Agreement for affiliated Partner with Great Lake – Northern Forest Cooperative Ecosystem Studies Unit (CESU) |
| Opportunity Category: | Discretionary |
| Opportunity Category Explanation: | – |
| Funding Instrument Type: | Cooperative Agreement |
| Category of Funding Activity: | Science and Technology and other Research and Development |
| Category Explanation: | – |
| Expected Number of Awards: | – |
| Assistance Listings: | 15.808 — U.S. Geological Survey Research and Data Collection |
| Cost Sharing or Matching Requirement: | No |
| Version: | Synopsis 1 |
| Posted Date: | Apr 14, 2022 |
| Last Updated Date: | Apr 14, 2022 |
| Original Closing Date for Applications: | May 14, 2022 Electronically submitted applications must be submitted no later than 5:00 p.m., ET, on the listed application due date. |
| Current Closing Date for Applications: | May 14, 2022 Electronically submitted applications must be submitted no later than 5:00 p.m., ET, on the listed application due date. |
| Archive Date: | – |
| Estimated Total Program Funding: | – |
| Award Ceiling: | $495,000 |
| Award Floor: | $79,500 |
Eligibility
| Eligible Applicants: | Others (see text field entitled “Additional Information on Eligibility” for clarification) |
| Additional Information on Eligibility: | This financial assistance opportunity is being issued under a Cooperative Ecosystem Studies Unit (CESU) Program. CESU’s are partnerships that provide research, technical assistance, and education. Eligible recipients must be a participating partner of the Great Lakes – Northern Forest Cooperative Ecosystem Studies Unit (CESU) Program. |
Additional Information
| Agency Name: | Geological Survey |
| Description: | The U.S. Geological Survey (USGS) is offering a funding opportunity to a CESU partner for research in developing deep convolutional neural networks for stream flow estimation and developing baseline Artificial Intelligence (hereafter, AI) models for flow estimation performance. Research Scientists at the EESC have a wealth of knowledge specific to ongoing studies that are using AI to estimate flow in freshwater streams. This research will advance our understanding by linking developing methods in computer science with ecological science. |
| Link to Additional Information: | – |
| Grantor Contact Information: | If you have difficulty accessing the full announcement electronically, please contact:
FAITH GRAVES
fgraves@usgs.gov Email:fgraves@usgs.gov |
Version History
| Version | Modification Description | Updated Date |
|---|---|---|
Related Documents
Folder 339488 Full Announcement-Full Announcement -> FUNDING OPPORTUNITY.pdf
Packages
| Agency Contact Information: | FAITH GRAVES fgraves@usgs.gov Email: fgraves@usgs.gov |
| Who Can Apply: | Organization Applicants |
| Assistance Listing Number | Competition ID | Competition Title | Opportunity Package ID | Opening Date | Closing Date | Actions |
|---|---|---|---|---|---|---|
| 15.808 | G22AS00314 | Cooperative Agreement for affiliated Partner with Great Lake – Northern Forest Cooperative Ecosystem Studies Unit (CESU) | PKG00273370 | Apr 14, 2022 | May 14, 2022 | View |
Package 1
Mandatory forms
339488 SF424_4_0-4.0.pdf
339488 ProjectNarrativeAttachments_1_2-1.2.pdf
339488 SF424A-1.0.pdf
339488 SF424B-1.1.pdf
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