Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

In this study, a novel approach combining fuzzy clustering and Least Squares Support Vector machine (LS-SVM) methods is developed for estimation of failure rate in water distribution networks and for determination of the relationship between failure rate-effective factors. For this aim, failure data observed Malatya water distribution network during 2006–2012 was selected as study area. In first phase, estimation model was developed and tested for the complete data set in estimating the failure rate by LS-SVM method. Then, in order to develop a more sensitive estimation model and to improve the performance of LS-SVM, 9 sub-regions were defined with similar characteristics by using fuzzy clustering method. Then failure rate estimation was carried out for each of the sub-regions using by LS-SVM method. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) methods were also used for estimation of failure rate and the results were compared with those of LS-SVM. The criteria such as Correlation Coefficient (R), Efficieny (E) and Root Mean Square Error (RMSE) were used to evaluate the performance of models. The results showed that LS-SVM model gives better results in comparison with the FFNN and GRNN models. It was also determined that LSSVM model results for the sub-regions defined by clustering analysis are better and that the clustering analysis increases the estimation model performance in addition to the fact that the estimation results have become better. In conclusion, it can be possible to develop a more sensitive estimation models using fuzzy clustering and LSSVM methods.