Deep Learning for Unmonitored Water Level Prediction and Risk Assessment

Details

• Creators:
  Corns, Steven M ; Long, Suzanna K ; Hale, Jacob ; Kanwar, Bhanu ; Vanfossan, Samuel
• Corporate Creators:
  Missouri University of Science and Technology. Dept. of Engineering Management and Systems Engineering
• Corporate Contributors:
  Missouri. Department of Transportation. Construction and Materials Division ; United States. Department of Transportation. Federal Highway Administration ; Mid-America Transportation Center
• Subject/TRT Terms:
  • Evacuation
  • Risk assessment
  • Water table
  • Planning
  • Geospatial data
  • Floods
  • Deep learning
  • Flood management
  • Evacuation routing and planning
• Publication/ Report Number:
  cmr 22-007
• Resource Type:
  Tech Report
• Geographical Coverage:
  Missouri ; United States
• Edition:
  Final Report (February 2021-June 2022)
• Corporate Publisher:
  Missouri. Department of Transportation. Construction and Materials Division
• Abstract:
  The research uses publicly available data to develop deep learning models to predict river gauge heights at unmonitored locations in Missouri. The geospatial and rainfall data for 20 different catchment areas of Missouri is used in tandem with the clustering and ensemble deep learning approaches to develop a high-performance deep learning model that efficiently captures the interdependencies between the time-series input data values. The models can accurately predict river water level values up to 4hours ahead in the future with a correlation of greater than 0.82 with most results having a correlation greater than 0.9. The data-based approach applied to develop a deep learning neural networks-based framework can assist the first responders in issuing timely and localized flood warnings for the safety of the general public. This methodology is applied to publicly available datasets obtained from The United States Geological Survey (USGS) and National Weather Service (NWS). The research project is funded by the Missouri Department of Transportation (MoDOT) and Mid-America Transportation Center (MATC).
• Format:
  PDF
• Funding:
  MoDOT project # TR202111
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/63364/dot_63364_DS1.pdf
• File Type:
  [PDF - 1.59 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:53a3ab1c5d2160496a2e9f748fd675d303817ca50f9545ef63381faf3f154f81c98218abb903266ea4515516f89fba5e85856f9e0e580e502183b3aa699f9b93