A review of leakage detection strategies for pressurised pipeline in steady-state

Water is a precious resource that should be managed carefully. However, due to leakages in water distributed networks (WDNs), a large amount of water is lost each year that suggests the need for reliable and robust leak detection and localization system. This paper attempts to review the current technologies for leakage detection in WDN as well as several proposed intelligent methodologies (such as support vector machine, neural network, and convolution neural network) over the past few years. The current methodologies and their limitations are discussed. Uncertainties involved in the implementation of WDN leakage detection are also discussed, and several suggestions to overcome such uncertainties are provided for future implementations.

Review of Current Technologies and Proposed Intelligent Methodologies for Water Distributed Network Leakage Detection

http://digital.csic.es/bitstream/10261/167067/1/Leak-Bayesian.pdf

Leak localization in water distribution networks using Bayesian classifiers

This research article presents a model-based framework for detecting and localizing leaks in water distribution networks (WDNs). The framework uses optimization and systematic search space ...

/pdf/leak-localization-in-a-real-water-distribution-network-based-8yad4wewq0.pdf

Leak Localization in a Real Water Distribution Network Based on Search-Space Reduction

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

/pdf/leak-localization-in-water-distribution-networks-using-4kia4rat7g.pdf

Lise Ferrandez-Gamot

Papers

Leak Localization in Water Distribution Networks using Pressure Residuals and Classifiers