Predictive Deep Learning for Flood Evacuation Planning and Routing

Corns, Steven M; Long, Suzanna K; Hale, Jacob; Kanwar, Bhanu; Gude, Vinayaka; Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering

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Predictive Deep Learning for Flood Evacuation Planning and Routing

2020-01-01
By Corns, Steven M ; Long, Suzanna K ; Hale, Jacob ; ...

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Details:

Creators:

Corns, Steven M ; Long, Suzanna K ; Hale, Jacob ; Kanwar, Bhanu ; Gude, Vinayaka

Corns, Steven M ; Long, Suzanna K ; Hale, Jacob ; Kanwar, Bhanu ; Gude, Vinayaka Less -
Corporate Creators:

Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Federal Highway Administration ; University of Nebraska. Mid-America Transportation Center

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Federal Highway Administration ; University of Nebraska. Mid-America Transportation Center Less -
Subject/TRT Terms:

[+]

Deep Learning Evacuation Evacuation Routing And Planning Flood Management Floods Forecasting Machine Learning Rainfall Routes And Routing Spatial Analysis
Publication/ Report Number:

25-1121-0005-130
Resource Type:

Tech Report
Geographical Coverage:

Missouri ; United States

Missouri ; United States Less -
Edition:

Final Report (January 1, 2019-December 31, 2019)
Corporate Publisher:

Missouri. Department of Transportation. Construction and Materials Division ; University of Nebraska-Lincoln. Mid-America Transportation Center

Missouri. Department of Transportation. Construction and Materials Division ; University of Nebraska-Lincoln. Mid-America Transportation Center Less -
Abstract:

This research was completed in tandem as a project funded through MoDOT and the MidAmerica Transportation Center. It used 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 were used to determine evacuation routing and detours to mitigate the potential for loss of life during flash floods. The project scope included analysis of publicly available flood data along the Meramec River basin in Fenton as part of a pilot project in Missouri. These data were then used to determine the rate of rise in order 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 were 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.
Format:

PDF
Funding:

69A3551747107
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:038a0e75024c5622a25dff077cbd4336214b863873ed8718fb1665080dbb2f04
Download URL:

https://rosap.ntl.bts.gov/view/dot/54547/dot_54547_DS1.pdf
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Predictive Deep Learning for Flood Evacuation Planning and Routing

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