| NSF Org: |
EAR Division Of Earth Sciences |
| Recipient: |
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| Initial Amendment Date: | August 5, 2015 |
| Latest Amendment Date: | October 21, 2019 |
| Award Number: | 1520870 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Margaret Benoit
mbenoit@nsf.gov (703)292-7233 EAR Division Of Earth Sciences GEO Directorate for Geosciences |
| Start Date: | August 15, 2015 |
| End Date: | July 31, 2020 (Estimated) |
| Total Intended Award Amount: | $1,975,000.00 |
| Total Awarded Amount to Date: | $1,991,000.00 |
| Funds Obligated to Date: |
FY 2016 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1960 KENNY RD COLUMBUS OH US 43210-1016 (614)688-8735 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1960 Kenny Road Columbus OH US 43210-1016 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
HDBE-Humans, Disasters, and th, EDUCATION AND WORKFORCE, SEES Hazards |
| Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.
The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project sought to create new data science tools that integrated information across multiple modalities while compensating for the uneven coverage in space and time. A core goal was to blend data from social sensors with traditional physical sensors as input into an integrated model to predict and prioritize the consequences of damage from hurricane induced flooding (e.g. flood mapping, storm surge) to people and civil infrastructure (e.g. transportation and power grid). Key project outcomes include:
The development of novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about infrastructure networks and domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns from citizen sensed information.
The design of state-of-the-art geospatial techniques for flood monitoring and management through human-guided machine learning algorithms for flood mapping using both active and passive satellite sensors and inundation modeling based on remote sensing, in-situ data, and storm surge models that inculcate hydrodynamic flow rates and parametric wind model forcings.
An advanced situational awareness tool (DisasterRecord) that combines insights from both of the above research strands. DisasterRecord fuses information from social media streams, active and passive satellite sensors, advanced storm surge and flood modeling , and traffic incidents of the affected area for a given disaster event. The visual integration is made possible through a new geocoding and georeferencing process (a form of registration) allowing first responders, humanitarian organizations, and governmental agencies on the ground to leverage it for response and recovery. tto increase the adaptation of the tool by humanitarian organizations and individual first responders, we modularized and re-engineered the pipeline to make it scalable and efficient (an early version of the tool successfully competed in IBM's Call for Code competition).
With regards to broader impacts, eight Ph.D. students (three women) and two postdoctoral scholars were trained on this grant. Several standalone tools were developed and presented at major publication venues including two award papers. Several tutorials, demonstrations and invited talks were presented by project personnel as part of a sustained outreach effort with humanitarian agencies and first responders.
Last Modified: 02/17/2021
Modified by: Srinivasan Parthasarathy
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