Data Anomaly Detection and Sediment Yield Estimation in the US Army Corps of Engineers’ Reservoir Sedimentation Information (RSI) Database

Archived

Dept. of the Army -- Corps of Engineers

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Description

The primary objective is to develop a method to identify erroneous data within the RSI system. Ideally, the investigator(s) will utilize machine learning algorithms to identify anomalies within the dataset. A secondary goal of the study is to use the RSI data, with supplementary data from other available data sources, to develop a machine-learning approach to estimate sedimentation rates. Research tasks should include: identifying appropriate supplemental data from other data sources; 2) identify any patterns and trends in the RSI data; 3) develop a machine-learning method to identify anomalies within the RSI data based on the composite dataset; and 4) develop a machine-learning method for estimating reservoir sedimentation rates.

Who can apply

  • Others

Contact

Chelsea M Whitten <br/>Grants Officer <br/>Phone 601-634-4679
chelsea.m.whitten@usace.army.mil

Key dates & funding
  • PostedJun 22, 2020
  • ClosesAug 24, 2020
  • Award floor$0
  • Award ceiling$40,000
  • Expected awards1
  • CFDA12.630
Categories
Science and Technology and other Research and Development

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