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Review of model-based and data-driven approaches for leak detection and location in water distribution systems
Author(s) -
Zukang Hu,
Beiqing Chen,
Wenlong Chen,
Debao Tan,
Dingtao Shen
Publication year - 2021
Publication title -
water science and technology water supply
Language(s) - English
Resource type - Journals
eISSN - 1607-0798
pISSN - 1606-9749
DOI - 10.2166/ws.2021.101
Subject(s) - leak detection , leak , computer science , data mining , environmental science , environmental engineering
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.

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