2423488
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: A Decision Support Platform for Urban Watershed Management and Water Infrastructure Planning
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project addresses the critical financing and complex operational challenges in managing water infrastructure by developing an open and extensible Artificial Intelligence (AI) platform that also enables enhanced interoperability and maintainability.
The proposed solution aims to enhance operational decision-making and facilitate innovative financing for essential water infrastructure by integrating models that also forecast the socio-economic impact of infrastructure actions alongside process-based and watershed models.
It aims to enhance infrastructure resilience, supporting sustainable urban growth and improving quality of life in urban areas.
It also is designed to promote social equity through more equitable distribution of water resources and improved access for underserved communities thereby supporting NSF's mission of providing technological leadership with broader societal impact.
Furthermore, the successful commercialization of this platform could generate significant economic benefits, including job creation and tax revenues.
The key technical innovation of this project lies in the development of an open AI software platform for water and wastewater utilities, which utilizes a multi-agent orchestration module to improve the accessibility and maintainability of digital and model-based tools.
Model integration driven by an innovative emulation approach, where a deep learning model is used to mimic the behavior of a physics-based model that allows for watershed scale simulations to be run at conversational speed.
This integration enables the creation of two AI tools designed to address infrastructure planning and operational needs.
The primary goals of the research are to successfully demonstrate interoperability of the AI platform, as well as demonstrate speed and accuracy of the neural networks emulating individual spatial elements of process-based models within a unified watershed-scale model.
The project will culminate in a year-long pilot of the AI platform and model-based tools in the DC Metro region.
This pilot aims to demonstrate the platform's value in operational settings and bring the software to a commercially ready state, ultimately providing actionable insights for water infrastructure management and supporting the development of more resilient infrastructure.
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.
Subawards are not planned for this award.
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project addresses the critical financing and complex operational challenges in managing water infrastructure by developing an open and extensible Artificial Intelligence (AI) platform that also enables enhanced interoperability and maintainability.
The proposed solution aims to enhance operational decision-making and facilitate innovative financing for essential water infrastructure by integrating models that also forecast the socio-economic impact of infrastructure actions alongside process-based and watershed models.
It aims to enhance infrastructure resilience, supporting sustainable urban growth and improving quality of life in urban areas.
It also is designed to promote social equity through more equitable distribution of water resources and improved access for underserved communities thereby supporting NSF's mission of providing technological leadership with broader societal impact.
Furthermore, the successful commercialization of this platform could generate significant economic benefits, including job creation and tax revenues.
The key technical innovation of this project lies in the development of an open AI software platform for water and wastewater utilities, which utilizes a multi-agent orchestration module to improve the accessibility and maintainability of digital and model-based tools.
Model integration driven by an innovative emulation approach, where a deep learning model is used to mimic the behavior of a physics-based model that allows for watershed scale simulations to be run at conversational speed.
This integration enables the creation of two AI tools designed to address infrastructure planning and operational needs.
The primary goals of the research are to successfully demonstrate interoperability of the AI platform, as well as demonstrate speed and accuracy of the neural networks emulating individual spatial elements of process-based models within a unified watershed-scale model.
The project will culminate in a year-long pilot of the AI platform and model-based tools in the DC Metro region.
This pilot aims to demonstrate the platform's value in operational settings and bring the software to a commercially ready state, ultimately providing actionable insights for water infrastructure management and supporting the development of more resilient infrastructure.
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.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Portland,
Oregon
97218-1726
United States
Geographic Scope
Single Zip Code
Maia Water was awarded
Cooperative Agreement 2423488
worth $989,932
from National Science Foundation in September 2024 with work to be completed primarily in Portland Oregon United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).
SBIR Details
Research Type
SBIR Phase II
Title
SBIR Phase II: A Decision Support Platform for Urban Watershed Management and Water Infrastructure Planning
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project addresses the critical financing and complex operational challenges in managing water infrastructure by developing an open and extensible Artificial Intelligence (AI) platform that also enables enhanced interoperability and maintainability. The proposed solution aims to enhance operational decision-making and facilitate innovative financing for essential water infrastructure by integrating models that also forecast the socio-economic impact of infrastructure actions alongside process-based and watershed models. It aims to enhance infrastructure resilience, supporting sustainable urban growth and improving quality of life in urban areas. It also is designed to promote social equity through more equitable distribution of water resources and improved access for underserved communities thereby supporting NSF's mission of providing technological leadership with broader societal impact. Furthermore, the successful commercialization of this platform could generate significant economic benefits, including job creation and tax revenues.
The key technical innovation of this project lies in the development of an open AI software platform for water and wastewater utilities, which utilizes a multi-agent orchestration module to improve the accessibility and maintainability of digital and model-based tools. Model integration driven by an innovative emulation approach, where a deep learning model is used to mimic the behavior of a physics-based model that allows for watershed scale simulations to be run at conversational speed. This integration enables the creation of two AI tools designed to address infrastructure planning and operational needs. The primary goals of the research are to successfully demonstrate interoperability of the AI platform, as well as demonstrate speed and accuracy of the the neural networks emulating individual spatial elements of process-based models within a unified watershed-scale model. The project will culminate in a year-long pilot of the AI platform and model-based tools in the DC Metro region. This pilot aims to demonstrate the platform's value in operational settings and bring the software to a commercially ready state, ultimately providing actionable insights for water infrastructure management and supporting the development of more resilient infrastructure.
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 23-516
Status
(Ongoing)
Last Modified 9/17/24
Period of Performance
9/1/24
Start Date
8/31/26
End Date
Funding Split
$989.9K
Federal Obligation
$0.0
Non-Federal Obligation
$989.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2423488
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
H3YAUAAPJAA7
Awardee CAGE
94ZA6
Performance District
OR-03
Senators
Jeff Merkley
Ron Wyden
Ron Wyden
Modified: 9/17/24