Application of support vector machine and relevance vector machine to determine evaporative losses in reservoir.

SelectedWorks Author Profiles:

Barnali Dixon

Document Type


Publication Date


Date Issued

January 2012

Date Available

June 2014




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.


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.





Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.