USF St. Petersburg campus Faculty Publications
Application of support vector machine and relevance vector machine to determine evaporative losses in reservoir.
Document Type
Article
Publication Date
2012
ISSN
0885-6087
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.
Language
en_US
Publisher
Wiley
Recommended Citation
Samui, P. & Dixon, B. (2012). Application of support vector machine and relevance vector machine to determine evaporative losses in reservoir. Hydrological Processes, 26(9), 1361-1369. DOI: 10.1002/hyp.8278
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Abstract only. Full-text article is available only through licensed access provided by the publisher. Published in Hydrological Processes, 26(9), 1361-1369. DOI: 10.1002/hyp.8278 Members of the USF System may access the full-text of the article through the authenticated link provided.