Abstract 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.
Deliverables
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Related Phases Phase II: Deep Learning Techniques for Flash Flood Management Phase III: Deep Learning for Unmonitored Water Level Prediction and Risk Assessment