Abstract
In this paper, the use of supervised classifiers for leak location in water distribution networks (WDN) is discussed. A comparative study is presented in the context of a benchmark network under the same leak and sensor placement scenarios. The comparison considers four classification tools widely used in the pattern recognition framework: Nearest Neighbor, Bayes Classifier, Artificial Neural Networks and Support Vector Machines. The classifiers’ selection is made by considering their different working principles and application advantages. Training and testing sets are formed by the residuals generated by using the EPANET hydraulic simulator. The robustness of the methods is compared with respect to the leak location performance under model parameter uncertainty, demand uncertainty, leak size uncertainty and sensor noise. The SVM performs similar or better than the other classifiers when all uncertainties are present.
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