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Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs
Author(s) -
Samui Pijush,
Dixon Barnali
Publication year - 2011
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.8278
Subject(s) - relevance vector machine , support vector machine , machine learning , artificial intelligence , computer science , statistical learning theory , relevance (law) , wind speed , meteorology , physics , political science , law
This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses ( E ) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E . The developed RVM model gives variance of the predicted E . A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E . Copyright © 2011 John Wiley & Sons, Ltd.