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A. Smola and B. Scholkopf, “On a Kernel Based Method for Pattern Recognition, Regression, Approximation and Operator Inversion,” Algorithmica, Vol. 22, 1998, pp. 211-231. doi:10.1007/PL00013831

has been cited by the following article:

• TITLE: Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs

  AUTHORS: Pijush Samui

  KEYWORDS: Evaporation Losses, Least Square Support Vector Machine, Prediction, Artificial Neural Network

  JOURNAL NAME: Engineering, Vol.3 No.4, April 8, 2011

  ABSTRACT: This article adopts Least Square Support Vector Machine (LSSVM) for prediction of Evaporation Losses (EL) in reservoirs. LSSVM is firmly based on the theory of statistical learning, uses regression technique. The input of LSSVM model is Mean air temperature (T) (?C), Average wind speed (WS)(m/sec), Sunshine hours (SH)(hrs/day), and Mean relative humidity(RH)(%). LSSVM has been used to compute error barn of predicted data. An equation has been developed for the determination of EL. Sensitivity analysis has been also performed to investigate the importance of each of the input parameters. A comparative study has been presented between LSSVM and artificial neural network (ANN) models. This study shows that LSSVM is a powerful tool for determination EL in reservoirs.