Literature review of data analytics for leak detection in water distribution networks: A focus on pressure and flow smart sensors

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 minimise 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, analysing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analysed and discussed. The smart sensor-based leakage detection methods can be categorised as data-driven methods and model-based methods. Data-driven methods can be further categorised as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorised 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 leakages 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; 5) spatial relations between sensors could provide more information and should be considered.