Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 10
Abstract
Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimize environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analyzed and discussed. The smart sensor-based leakage detection methods can be categorized as data-driven methods and model-based methods. Data-driven methods can be further categorized as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorized as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: (1) most of the leakage detection methods are focused on burst detection, and different types of leakage should be considered in future research, (2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed, (3) it is important to consider hydraulic model uncertainties, (4) unrealistic assumptions should be addressed in future research, and (5) spatial relations between sensors could provide more information and should be considered.
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Data Availability Statement
No data, models, or code were generated or used during the study.
Acknowledgments
The first author is funded by the China Scholarship Council (No. 202006370080), and the work is supported by a Royal Academy of Engineering Industrial Fellowship to resource Raziyeh Farmani’s involvement (IF\192057).
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Journal of Water Resources Planning and Management
Volume 148 • Issue 10 • October 2022
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Published online: Jul 27, 2022
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