A Systematic Review of Computational Approach to Pipeline Leakage Detection in a Water Distribution Network

Abstract

The detection and localization of leakages in water distribution networks is crucial for both the conservation of
resources and the efficient operation. The process network has proved to be a difficult task over years,
considering the complexities inherent in water distribution networks. The enormous interconnected pipelines
make the leakage detection and location process burdensome. Computational techniques play a significant role in
this domain by offering advanced tools and techniques for leakage detection. This study, therefore, performed a
systematic review of published articles on computational leakage detection and localization in a water
distribution network. Findings show the number of recent quality studies on the computational approach to water
distribution network leakage research is beginning to dwindle, considering the journal's impact factor. In the
recent studies, a deep learning algorithm is beginning to trend as the most significant computational technique,
as it accounts for 13.21 % (n=7) of the pipeline leakage research output. The univariable predicated studies
account for 83.33%of the research output disseminated in the past five years. The invention of various efficient
learning methods and network structures in deep learning algorithms makes it suitable for the realization of
multi-disciplinary studies, as the multi-variable concept will reduce false positives and negatives, enhancing the
overall reliability of leak detection and localization models in future studies.