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. This data was 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.

VIEW FINAL REPORT

Report number: cmr 20-002
Published: March 2020
Project number: TR201912
Authors: Dr. Steven Corns and Dr. Suzanna Long
Performing organization: Missouri University of Science and Technology