Year Established: 2013 Start Date: 2013-03-01 End Date: 2016-02-28
Total Federal Funds: $32,000 Total Non-Federal Funds: $64,000
Principal Investigators: Ramesh Teegavarapu
Abstract: The main objective of the proposed research study is to develop and evaluate several statistical and data-mining based algorithms for identification and detection of outliers and data anomalies in hydrometeorological data. This study in its first phase will focus on stage data obtained from different hydraulic structures in SFWMD region from different sensors. The algorithms applicable in a standard test environment will help in the evaluation of data anomalies that would ultimately be used to improve the stage data. The proposed study would provide a complete evaluation of existing methods of data outlier detection. The methods will be evaluated at different hydraulic structures and will be evaluated using a number of indices and skill scores including input from modelers and data management personnel at SFWMD. The study will use data from a network of stage monitoring sensors automatically uploaded into the databases of SFWMD. The proposed research is highly relevant and critical to a number of water resources management agencies (e.g. South Florida Water Management District (SFWMD)) that currently stage data for modeling and management of day-to-day operations of water resources systems and development of protocols for flood control warnings. The products derived from the proposed study are expected to be tested for real-time evaluation of stage data by South Florida Water Management District (SFWMD).