Soft computing techniques toward modeling the water supplies of Cyprus
This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of Germasogeia mountainous watersheds in Cyprus. Initially, ? ? -Regression Support Vector Machines (? ? -RSVM) and fuzzy weighted ? ? -RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers.
water supply support vector machines artificial neural networks 5-fold cross validation kernel algorithms