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Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP.
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This content will become publicly available on July 1, 2026
Pressure Sensor Placement in Water Distribution Systems Using Leak Resolution–Specific Surrogates for Leak Localization
The effectiveness of model-based leak localization methods in water distribution systems (WDSs), including optimization-based and machine learning approaches, significantly depends on the quality and quantity of input data. Pressure data, easily accessible due to nonintrusive sensor installation and maintenance, are commonly used. However, economic constraints limit the number of sensors in WDSs, highlighting the need for strategic sensor placement to enhance data quality. This study introduces a novel, method-independent sensor placement strategy that integrates cluster definitions (leak resolution) with intuitive surrogates for localization performance, addressing the limitations of existing methods reliant on complex, nonintuitive metrics. We propose the Euclidean cluster-based optimal placement of sensors (ECOPS) approach, which employs sensitivity and uniqueness as fundamental signal properties to guide sensor placement. Validation tests within a comprehensive real-world WDS demonstrate that ECOPS outperforms existing surrogate-based approaches and improves the performance of current sensors installed for leak characterization. These findings provide compelling evidence of ECOPS’s potential for enhancing pressure sensor placement, thereby improving leak localization in WDS applications.
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- Award ID(s):
- 1763028
- PAR ID:
- 10599576
- Publisher / Repository:
- ASCE
- Date Published:
- Journal Name:
- Journal of Water Resources Planning and Management
- Volume:
- 151
- Issue:
- 7
- ISSN:
- 0733-9496
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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