Uncertainties in different leak localization methods for water distribution networks: a review

Abstract

ABSTRACT In recent decades, research on leak detection and localization in water distribution networks has been an area of growing interest in both water management and fault detection. In the literature, numerous leak localization techniques were developed from model-based methods (such as steady-state and quasi-steady state) and data-driven/machine learning models (e.g. time series modeling, prediction, and classification). However, there is still a need for study on the definition and enumeration of various sources, types, and nature of uncertainties in leak localization modelling processes. In the context of steady-state analysis, this review paper’s main objective is to list the uncertainties related to model-based, data-driven and hybrid methods. This review outlines that, for the three classes of methods, the interplay of uncertainties with the modelling approximations jointly influences the localization performance and are often overlooked. Furthermore, realization of modelling assumptions and error propagation is needed for a successful real-world implementation.