Leak localization in water distribution networks using fusion classifier techniques

ABSTRACT This article presents a model-based fusion classifier technique to estimate the approximate position of a leak in a water distribution network (WDN). This technique uses residuals obtained by comparing pressure measurements with the nominal behavior estimated from a mathematical model. These residuals are analyzed as a classification task to associate the information with an approximate leak location. The classification task is performed by three different classifiers: the K-Nearest Neighbors algorithm, the Multilayer Perceptron, and the Decision Tree classifier. The outputs of these classifiers are combined using two ensemble/fusion methods, the Majority Voting and the Naive Bayes fusion, to improve the accuracy of the leak position estimation. Results from a benchmark problem comprising 126 nodes, 8 valves, 2 tanks, 2 reservoirs, 3 pumps, and 168 pipes, subject to four variable demand patterns, illustrate the performance improvement of the two ensemble methods over an individual classifier. Additionally, to create a more realistic scenario, the performance of the proposed leak localization scheme is evaluated in a perturbed scenario by noise and uncertainties in the demand estimation.