Pressure-Leak Duality for Leak Detection and Localization in Water Distribution Systems

Abstract A significant percentage of treated water is lost due to leakage in water distribution systems. The state-of-the-art leak detection and localization schemes use a hybrid approach of hydraulic modeling and data-driven techniques. Most of these works, however, focus on single leakage detection and localization. In this research, we propose to use combined pressure and flow residual data to detect and localize multiple leaks. The proposed approach has two phases: detection and localization. The detection phase uses the combination of pressure and flow residuals to build a hydraulic model and classification algorithm to identify leaks. The localization phase analyzes the pattern of isolated leak residuals to localize multiple leaks. To evaluate the performance of the proposed approach, we conducted experiments using Hanoi Water Network benchmark and a dataset produced based on LeakDB benchmark's dataset preparation procedure. The result for a well-calibrated hydraulic model shows that leak detection is 100% accurate while localization is 90% accurate, thereby outperforming minimum night flow and raw- and residual-based methods in localizing leaks. The proposed approach performed relatively well with the introduction of demand and noise uncertainty. The proposed localization approach is also able to locate two to four leaks that existed simultaneously.

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Review of model-based and data-driven approaches for leak detection and location in water distribution systems

Water Science & Technology Water Supply ◽

10.2166/ws.2021.101 ◽

2021 ◽

Author(s):

Zukang Hu ◽

Beiqing Chen ◽

Wenlong Chen ◽

Debao Tan ◽

Dingtao Shen

Keyword(s):

Distribution Systems ◽

Water Distribution ◽

Water Distribution Systems ◽

Leak Detection ◽

Data Driven ◽

Detection Methods ◽

Water Utility ◽

Model Based ◽

Utility Companies ◽

Leak Detection And Location

Abstract 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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Experiments based comparative evaluations of machine learning techniques for leak detection in water distribution systems

Water Science & Technology Water Supply ◽

10.2166/ws.2021.248 ◽

2021 ◽

Author(s):

Maryam Kammoun ◽

Amina Kammoun ◽

Mohamed Abid

Keyword(s):

Machine Learning ◽

Distribution Systems ◽

Water Distribution ◽

Water Distribution Systems ◽

Leak Detection ◽

Distribution Networks ◽

Machine Learning Techniques ◽

Support Vector ◽

Flow Data ◽

Learning Techniques

Abstract Leakage in water distribution systems is a significant long-standing problem due to the huge economic and ecological losses. Different leak detection studies have been examined in literature using different types of technologies and data. Currently, although machine learning techniques have achieved tremendous progress in outlier detection approaches, they are still limited in terms of water leak detection applications. This research aims to improve the leak detection performances by refining the choices of learning data and techniques. From this perspective, commonly used techniques for leak detection are assessed in this paper, and the characteristics of hydraulic data are investigated. Four intelligent algorithms are compared, namely k-nearest neighbors, support vector machines, logistic regression, and multi-layer perceptron. This study focuses on six experiments based on identifying outliers in various packages of pressure and flow data, yearly data, seasonal data, night data, and flow data difference to detect leakage in water distribution networks. Different scenarios of realistic water demand in two networks from the benchmark dataset LeakDB are used. Results demonstrate that the leak detection accuracy varies between 30% and 100% depending on the experiment and the choices of algorithms and data.

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