Deep Learning for Unmonitored Water Level Prediction and Risk Assessment

This project uses deep learning and other computational intelligence methods to leverage public geospatial data and historical National Oceanic and Atmospheric Administration (NOAA) data to develop forecasting tools to create virtual water level monitors. These tools inform existing models developed in previous Mid-America Transportation Center/Missouri Department of Transportation (MATC/MoDOT) projects for flood prediction and models developed by the United States Geological Survey (USGS), Federal Emergency Management Agency (FEMA), NOAA, and others and are used to reduce the errors from these models due to sparse data for prediction. The project scope includes a survey instrument to gather data from first responders who are required to travel during these hazardous events. These data are then used to determine the water levels and rate of change at unmonitored sites based on projected rainfall totals based on drainage basin information and recent weather patterns. The data from these virtual monitors is then used for flood event prediction to improve accuracy. The results of these virtual monitors will be validated by manual testing at prediction locations. In addition, the data from the virtual monitors and the validation readings will be used to determine the sources of uncertainty in the predictions and recommend where physical monitors should be placed to improve future predictions. This provides the transportation safety or disaster planner increased accuracy to better plan for flooding events.

Language

• English

Project

• Status: Active
• Funding: $300000
• Contract Numbers:

  TR202111

  69A3551747107

• Sponsor Organizations:

  Missouri Department of Transportation

  1617 Missouri Boulevard
  P.O. Box 270
  Jefferson City, MO  United States  65102

  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:

  Harper, Jennifer

  Engelrecht, Chris

• Performing Organizations:

  Missouri University of Science & Technology, Rolla

  Department of Engineering
  202 University Center
  Rolla, MO    65409

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Principal Investigators:

  Corns, Steven

  Long, Suzanna

• Start Date: 20210208
• Expected Completion Date: 20220630
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Accuracy; Data collection; First responders; Floods; Forecasting; Machine learning; Rainfall; Risk assessment; Uncertainty; Validation
• Subject Areas: Data and Information Technology; Geotechnology; Highways; Hydraulics and Hydrology; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01769437
• Record Type: Research project
• Source Agency: Missouri Department of Transportation
• Contract Numbers: TR202111, 69A3551747107
• Files: UTC, RIP, STATEDOT
• Created Date: Apr 14 2021 10:23AM