Award Abstract # 2033607
A1: The Urban Flooding Open Knowledge Network (UF-OKN): Delivering Flood Information to AnyOne, AnyTime, AnyWhere

NSF Org: ITE
Innovation and Technology Ecosystems
Recipient: CINCINNATI UNIV OF
Initial Amendment Date: August 17, 2020
Latest Amendment Date: July 25, 2023
Award Number: 2033607
Award Instrument: Cooperative Agreement
Program Manager: Jemin George
jgeorge@nsf.gov
 (703)292-2251
ITE
 Innovation and Technology Ecosystems
TIP
 Dir for Tech, Innovation, & Partnerships
Start Date: September 1, 2020
End Date: August 31, 2024 (Estimated)
Total Intended Award Amount: $4,999,998.00
Total Awarded Amount to Date: $5,299,998.00
Funds Obligated to Date: FY 2020 = $2,853,561.00
FY 2021 = $2,146,437.00

FY 2022 = $300,000.00
History of Investigator:
  • Lilit Yeghiazarian (Principal Investigator)
    yeghialt@ucmail.uc.edu
  • Sankarasubraman Arumugam (Co-Principal Investigator)
  • Ximing Cai (Co-Principal Investigator)
  • Venkatesh Merwade (Co-Principal Investigator)
  • Torsten Hahmann (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Cincinnati Main Campus
2600 CLIFTON AVE
CINCINNATI
OH  US  45220-2872
(513)556-4358
Sponsor Congressional District: 01
Primary Place of Performance: University of Cincinnati
2901 Woodside Dr 746 ERC
Cincinnati
OH  US  45221-0012
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): DZ4YCZ3QSPR5
Parent UEI: DZ4YCZ3QSPR5
NSF Program(s): CA-HDR: Convergence Accelerato,
Convergence Accelerator Resrch
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 095Y00, 131Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.083, 47.084

ABSTRACT

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 II project is to minimize economic and human losses from future urban flooding in the United States. Floods impact a series of interconnected urban systems ? the Urban Multiplex, that include the power grid and transportation network, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, all of which are intertwined with the socioeconomic and public health sectors. This project uses a convergent approach to integrate these multiple interconnected systems and merges state-of-the-art practices in hydrological and hydraulic engineering; systems analysis, optimization and control; machine learning, data and computer science; epidemiology; socioeconomics; and transportation and electrical engineering to develop an Urban Flood Open Knowledge Network (OKN), the final deliverable of the project. It will be built with unprecedented engagement between urban domain and flooding experts, practitioners, scientists, and technology specialists. This partnership includes universities, nonprofits, private companies, national labs, federal agencies, states, counties and municipalities across the country. The Urban Flood-OKN will empower decision makers and the general public by providing information on how much flooding may occur from a future event and will also show its cascading impact on natural and engineered infrastructures of an urban area.

The convergence research and development team supporting this effort has integrated researchers and methods from across disciplines including civil and environmental engineering, hydrology, geography, computer science, meteorology, public safety, emergency response, and economics. The partners engaged as advisors, potential users, and developers include more than a dozen municipalities and water management districts, federal agencies (NOAA, USDOT, NIST, USGS, EPA, FEMA), a national lab (PNNL), non-profits (Consortium of Ocean Leadership, Woods Hole Oceanographic Institution, Consortium of Universities for the Advancement of Hydrologic Science), for-profit organizations, consortia, and individuals.

The real impact of flooding on the Urban Multiplex is currently very difficult to quantify because many of its systems are independently designed and managed. Hence an open knowledge network that captures the interconnectedness of these systems and how they impact each other is critically needed. This project will semantically link the Urban Multiplex, whose subsystems generate data that are currently not interoperable. This will enable meaningful queries on flood-related information relevant to urban sustainability. The Urban Flood-OKN will help increase urban resilience and minimize damage from future urban floods due to changing climate and changing land use patterns. It will allow identification of early-warning signals of critical transitions/shifts of a complex interdependent infrastructure system responding to external pressures, and how shifts will be affected by the structure of the Urban Multiplex and failures propagating across its subsystems. This project also has the potential to bring about a societal transformation in the way practitioners, researchers, and the general public engage with, consume, and act upon information about the potential response of the Urban Multiplex to extreme external pressures. This project will allow internet queries that produce actionable information on what to do during storms and flooding, how to plan long-term, and how these decisions will contribute to urban sustainability and resilience ? all based on solid science.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Hahmann, Torsten and Powell, Robert "Automatically Extracting OWL Versions of FOL Ontologies" International Semantic Web Conference (ISWC 2021) , 2021 Citation Details
Xu, Tianle and Merwade, Venkatesh and Wang, Zhiquan "Interpolating Hydrologic Data Using Laplace Formulation" Remote Sensing , v.15 , 2023 https://doi.org/10.3390/rs15153844 Citation Details
Li, Shaobin and Cai, Ximing and Emaminejad, Seyed Aryan and Juneja, Ankita and Niroula, Sundar and Oh, Seojeong and Wallington, Kevin and Cusick, Roland D. and Gramig, Benjamin M. and John, Stephen and McIsaac, Gregory F. and Singh, Vijay "Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds" Environmental Modelling & Software , v.143 , 2021 https://doi.org/10.1016/j.envsoft.2021.105083 Citation Details
Ma, T. and Barajas-Solano, David A. and Huang, R. and Tartakovsky, Alexandre M. "ELECTRIC LOAD AND POWER FORECASTING USING ENSEMBLE GAUSSIAN PROCESS REGRESSION" Journal of Machine Learning for Modeling and Computing , v.3 , 2022 https://doi.org/10.1615/JMachLearnModelComput.2022041871 Citation Details
Johnson, J. Michael and Narock, Tom and Singh?Mohudpur, Justin and Fils, Doug and Clarke, Keith C. and Saksena, Siddharth and Shepherd, Adam and Arumugam, Sankar and Yeghiazarian, Lilit "Knowledge graphs to support real?time flood impact evaluation" AI Magazine , v.43 , 2022 https://doi.org/10.1002/aaai.12035 Citation Details

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