Support vector machines-kernel algorithms for the estimation of the water supply in Cyprus

Abstract

This research effort aimed in the estimation of the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. The actual target was the development of an ε-Regression Support Vector Machine (SVMR) system with five input parameters. The 5-Fold Cross Validation method was applied in order to produce a more representative training data set. The fuzzy-weighted SVR combined with a fuzzy partition approach was employed in order to enhance the quality of the results and to offer an optimization approach. The final models that were produced have proven to perform with an error of very low magnitude in the testing phase when first time seen data were used. © 2010 Springer-Verlag Berlin Heidelberg.