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

The summary for the Data Anomaly Detection and Sediment Yield Estimation in the US Army Corps of Engineers' Reservoir Sedimentation Information (RSI) Database grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Dept of the Army Corps of Engineers, which is the U.S. government agency offering this grant.

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.