Computer Science and Engineering, Department of


Date of this Version

Winter 11-1-2012


A. Mohan, P. Z. Revesz, Temporal data mining of uncertain water reservoir data, Proc. 3rd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data, ACM Press, pp. 10-17, Redondo Beach, CA, USA, November 2012.


Abhinaya Mohan, M.S. in Computer Science, University of Nebraska-Lincoln, 2014.

This paper is authored by an employee(s) of the U.S. Government and is in the public domain


This paper describes the challenges of data mining uncertain water reservoir data based on past human operations in order to learn from them reservoir policies that can be automated for the future operation of the water reservoirs. Records of human operations of water reservoirs often contain uncertain data. For example, the recorded amounts of water released and retained in the water reservoirs are typically uncertain, i.e., they are bounded by some minimum and maximum values. Moreover, the time of release is also uncertain, i.e., typically only monthly or weekly amounts are recorded. To increase the effectiveness of data mining of uncertain water reservoir data, temporal data mining with inflow and rainfall data from several prior months was used. The experiments also compared several different data classification methods for robustness in the case of uncertain data.