Predictive Deep Learning for Flood Evacuation Planning and Routing
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 realtime 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
Corns, S;et al.
Mid-America Transportation Center; University of Nebraska-Lincoln
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