Estimating Road Inundation Levels Due to Recurrent Flooding from Image Data

The frequency of nuisance flooding events according to a recent report by the National Oceanic and Atmospheric Administration (NOAA) is increasing and accelerating in many locations especially along much of the U.S. East Coast which is attributable to Sea Level Rise (SLR). Based on median values of relative SLR projections, by 2050, the majority of US coastal cities will experience recurrent flooding thirty or more days per year due to the accelerating impacts of SLR. Whereas monitoring conditions of all public-serving infrastructures is important under the threat of flooding, it is particularly valuable to know the inundations on roadways since many societal functions depend on a functioning transportation infrastructure including routing of emergency vehicles and delivery of goods and services to support commerce. Today, there is no effective system for monitoring inundations in near real-time for large-scale transportation networks and for communities that are particularly vulnerable to SLR (e.g., Hampton Roads, Virginia, Wilmington, Delaware). In addition, there is no exiting system to collect, archive, and automatically analyze inundation levels effectively for large-scale networks. Such data are essential for making both operational and planning decisions. Accurate and rich datasets for quantifying the impacts of flooding are necessary for developing sustainable and resilient policies and solutions. Overall, this research proposes to develop fundamental methodologies, algorithms, and predictive capabilities to survey and estimate water inundations due to flooding based on image data. Even though the images for this research will primarily be obtained from video surveillance cameras, the principles and methodologies developed will be applicable to other image sources (e.g., from smartphones, social media). Specific research goals are: (i) Collection of a large image dataset to support the estimation of inundation levels; (ii) Development of machine learning algorithms to extract inundation levels from image data; (iii) Evaluation of the accuracy of the algorithms in predicting inundation levels under different conditions; (iv) Development of data-driven methods for predicting future inundation levels based on various factors (e.g., expected and past rainfall, drainage, topography). After the basic tools are developed, the research team plans to evaluate them based on images collected during an actual flooding event(s) in Norfolk, VA.

    Language

    • English

    Project

    • Status: Active
    • Sponsor Organizations:

      Old Dominion University

      Norfolk, VA  United States  23529

      University of Virginia, Charlottesville

      Center for Transportation Studies
      P.O. Box 400742, Thornton Hall, D228
      Charlottesville, VA  United States  22903

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Mid-Atlantic Transportation Sustainability Center

      University of Virginia
      Charlottesville, VA  United States 
    • Performing Organizations:

      Old Dominion University

      Norfolk, VA  United States  23529

      University of Virginia, Charlottesville

      Center for Transportation Studies
      P.O. Box 400742, Thornton Hall, D228
      Charlottesville, VA  United States  22903
    • Principal Investigators:

      Cetin, Mecit

      Iftekharuddin, Khan

      Goodall, Jonathan

    • Start Date: 20170501
    • Expected Completion Date: 20181031
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01645915
    • Record Type: Research project
    • Source Agency: Mid-Atlantic Transportation Sustainability Center
    • Files: UTC, RiP
    • Created Date: Sep 11 2017 12:06PM