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.

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

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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Literature Review of Data Analytics for Leak Detection in Water Distribution Networks: A Focus on Pressure and Flow Smart Sensors

Leakages in water distribution networks (WDNs) are estimated to globally cost 39 billion USD/year and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages. 

A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks

In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isola...

Leakage Detection and Localization in a Water Distribution Network through Comparison of Observed and Simulated Pressure Data

In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolation Methods (BattLeDIM). The method is based on the application of the hydraulic model of the water distribution network and the comparison of simulated pressures against the corresponding values observed in field. In particular, it consists of two phases: (1) calibration of the hydraulic model of the network; and (2) detection and localization of the leakages affecting the water distribution network through the application of engineering judgment and the adoption of an enumerative procedure. The method was applied to the case-study network of L-Town, enabling 16 of 23 leakages to be efficiently detected and 11 of these also accurately localized. The proposed method is simple and transparent and can aid water utilities in water leakage management. 

In many modern water networks, an emerging trend is to measure pressure at various points in the network for operational reasons. Because leaks typically induce a signature on pressure, the...

Venkata Siva Areti

Papers

Successive Linear Approximation Methods for Leak Detection in Water Distribution Systems