Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs |
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| Authors: | Pijush Samui Barnali Dixon |
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| Institution: | 1. Associate Professor, Centre for Disaster Mitigation and Management, VIT University, Vellore‐632014, India;2. Associate Professor, Geo‐Spatial Analytics Lab, Dept. of Environmental Science, University of South Florida, St. Petersburg 140 Seventh Ave South, St. Petersburg, Fl 33701, USA |
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| Abstract: | 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. |
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| Keywords: | evaporation losses Support Vector Machine Relevance Vector Machine Artificial Neural Network prediction |
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