Review of model-based and data-driven approaches for leak detection and location in water distribution systems
Zukang Hu,
Beiqing Chen,
Wenlong Chen,
Debao Tan,
Dingtao Shen
Affiliations
Zukang Hu
College of Computer and Information, Hohai University, Nanjing 210098, China
Beiqing Chen
Hubei Provincial Key Laboratory of River Basin Water Resources and Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan 430310, China and Spatial Information Technology Application Department, Changjiang River Scientific Research Institute, Wuhan 430310, China
Wenlong Chen
Hubei Provincial Key Laboratory of River Basin Water Resources and Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan 430310, China and Spatial Information Technology Application Department, Changjiang River Scientific Research Institute, Wuhan 430310, China
Debao Tan
College of Computer and Information, Hohai University, Nanjing 210098, China
Dingtao Shen
Hubei Provincial Key Laboratory of River Basin Water Resources and Eco-Environmental Sciences, Changjiang River Scientific Research Institute, Wuhan 430310, China and Spatial Information Technology Application Department, Changjiang River Scientific Research Institute, Wuhan 430310, China
Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands. HIGHLIGHTS A comprehensive review of model-based and data-driven approaches for leak detection.; Generic framework of each technique is summarized and compared.; Future directions for improving the performance of leak detection is presented.;
