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Data Anomaly Detection and Sediment Yield Estimation in the US Army Corps of Engineers’ Reservoir Sedimentation Information (RSI) Database
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.
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.
Agency: Department of Defense
Office: Dept. of the Army -- Corps of Engineers
Estimated Funding: $2,500,000
Office: Dept. of the Army -- Corps of Engineers
Estimated Funding: $2,500,000
Obtain Full Opportunity Text:
Link to Opportunity in SAMS Domestic - Updated
Additional Information of Eligibility:
This opportunity is restricted to non-federal partners of the Great Rivers Cooperative Ecosystems Studies Unit (CESU)
Full Opportunity Web Address:
https://mygrants.servicenowservices.com/grants?id=grants_funding_opportunity&table=u_domestic_funding_opportunity&sys_id=47dd8ab51bad10500f7aeb5ce54bcb0f&view=Default
Contact:
Agency Email Description:
Grants Officer
Agency Email:
Date Posted:
2020-06-22
Application Due Date:
Archive Date:
2020-09-14
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