Predictive Deep Learning for Flash Flood Management
This research uses deep learning methods, along with geospatial data from the USGS National Map and other public geospatial data sources, to develop forecasting tools capable of assessing water level rate of change in high risk flood areas. These tools build on existing models developed by the USGS, FEMA, and others, and are used to determine evacuation routing and detours to mitigate the potential for loss of life during flash floods. The project scope includes analysis of publically available flood data along a river basin as part of a pilot project in Missouri. These data are then used to determine the rate of rise based on projected rainfall totals. This rate of rise is used to model evacuation or detour planning modules that can be implemented to assure the safety of the community and highway personnel, as well as the safe and secure transport of goods along public roadways. These modules can be linked to existing real-time rainfall gauges and weather forecasts for improved accuracy and usability. The transportation safety or disaster planner can use these results to produce planning documents based on geospatial data and information to develop region-specific tools and methods.
Language
- English
Project
- Status: Completed
- Funding: $97500
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Contract Numbers:
69A3551747107
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Mid-America Transportation Center
University of Nebraska-Lincoln
2200 Vine Street, PO Box 830851
Lincoln, NE United States 68583-0851 -
Project Managers:
Stearns, Amy
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Performing Organizations:
Missouri University of Science & Technology, Rolla
Department of Engineering
202 University Center
Rolla, MO 65409 -
Principal Investigators:
Corns, Steven
Long, Suzanna
- Start Date: 20200101
- Expected Completion Date: 20201231
- Actual Completion Date: 20201231
- USDOT Program: University Transportation Centers Program
- Source Data: RiP Project 91994-56
Subject/Index Terms
- TRT Terms: Evacuation; Floods; Forecasting; Geography; Information systems; Location; Machine learning; Rainfall; Routing; Water table
- Subject Areas: Data and Information Technology; Highways; Hydraulics and Hydrology; Operations and Traffic Management; Planning and Forecasting; Security and Emergencies;
Filing Info
- Accession Number: 01747710
- Record Type: Research project
- Source Agency: Mid-America Transportation Center
- Contract Numbers: 69A3551747107
- Files: UTC, RIP
- Created Date: Aug 7 2020 5:44PM