EarthRef.org Reference Database (ERR)
Development and Maintenance by the EarthRef.org Database Team

Detailed Reference Information
Khalil et al. 2005
Khalil, A., McKee, M., Kemblowski, M. and Asefa, T. (2005). Sparse Bayesian learning machine for real-time management of reservoir releases. Water Resources Research 41: doi: 10.1029/2004WR003891. issn: 0043-1397.

Water scarcity and uncertainties in forecasting future water availabilities present serious problems for basin-scale water management. These problems create a need for intelligent prediction models that learn and adapt to their environment in order to provide water managers with decision-relevant information related to the operation of river systems. This manuscript presents examples of state-of-the-art techniques for forecasting that combine excellent generalization properties and sparse representation within a Bayesian paradigm. The techniques are demonstrated as decision tools to enhance real-time water management. A relevance vector machine, which is a probabilistic model, has been used in an online fashion to provide confident forecasts given knowledge of some state and exogenous conditions. In practical applications, online algorithms should recognize changes in the input space and account for drift in system behavior. Support vectors machines lend themselves particularly well to the detection of drift and hence to the initiation of adaptation in response to a recognized shift in system structure. The resulting model will normally have a structure and parameterization that suits the information content of the available data. The utility and practicality of this proposed approach have been demonstrated with an application in a real case study involving real-time operation of a reservoir in a river basin in southern Utah.

BACKGROUND DATA FILES

Abstract

Keywords
Hydrology, Modeling, Hydrology, Reservoirs (surface), Hydrology, Water management, Hydrology, Computational hydrology, Hydrology, Estimation and forecasting, Bayesian, probabilistic machines, statistical learning theory, real-time management, reservoir releases
Journal
Water Resources Research
http://www.agu.org/wrr/
Publisher
American Geophysical Union
2000 Florida Avenue N.W.
Washington, D.C. 20009-1277
USA
1-202-462-6900
1-202-328-0566
service@agu.org
Click to clear formClick to return to previous pageClick to submit