LEAK DETECTION IN SIMULATED WATER PIPE NETWORKS USING SVM

Abstract

The detection and location of leaks in water pipe networks is a significant problem, which would benefit from more effective solutions. The information about the presence and location of leaks in a pipe network could be contained in the distribution of pressure or flow values at various points in the network; however, the information is encoded in such a way that its extraction is a complex inverse engineering problem. Such problems can be solved effectively through the use of pattern recognition techniques such as artificial neural networks (ANNs) or support vector machines (SVMs). This article presents a method of using SVM analysis to interpret the data obtained by a collection of pressure sensors or flow-measuring devices monitoring a pipe network in order to obtain information about the location and size of leaks in the network.