Premium
Sparse Bayesian learning machine for real‐time management of reservoir releases
Author(s) -
Khalil Abedalrazq,
McKee Mac,
Kemblowski Mariush,
Asefa Tirusew
Publication year - 2005
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2004wr003891
Subject(s) - computer science , machine learning , concept drift , bayesian probability , generalization , probabilistic logic , artificial intelligence , representation (politics) , bayesian network , toolbox , decision support system , bayesian inference , relevance (law) , adaptation (eye) , data mining , data stream mining , mathematical analysis , physics , mathematics , optics , politics , political science , law , programming language
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.