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- Title
Prediction of Rainfall Using Support Vector Machine and Relevance Vector Machine.
- Authors
Samui, Pijush; Mandla, Venkata Ravibabu; Krishna, Arun; Teja, Tarun
- Abstract
This article adopts Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of rainfall in Vellore (India). SVM is firmly based on the theory of statistical learning theory. RVM is a probabilistic basis model. SVM and RVM use air temperature (T), sunshine, humidity and wind speed (Va) as input variables. This article uses SVM and RVM as a regression technique. Equations have been also developed for prediction of rainfall. The developed RVM gives variance of the predicted rainfall. This study shows the RVM is more robust model than the SVM.
- Subjects
VELLORE (India); INDIA; RAINFALL; RAINFALL reliability; PROBABLE maximum precipitation (Hydrometeorology); HUMIDITY; WIND speed
- Publication
Earth Science India, 2011, Vol 4, Issue 4, p188
- ISSN
0974-8350
- Publication type
Article