Journal article
Leak detection in simulated water pipe networks using SVM
J Mashford, D De Silva, S Burn, D Marney
Applied Artificial Intelligence | TAYLOR & FRANCIS INC | Published : 2012
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 pressur..
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Funding Acknowledgements
Funding for this research was provided by the SEQ Urban Water Security Research Alliance. The authors would like to thank Mike Rahilly for programming work on this project and for valuable discussions. The authors would also like to thank Don Begbie, John Vitkovsky and Charles Lemckert for very helpful comments.