A Hybrid Machine Learning Algorithm for Pipeline Leak Detection and Localisation in Water Distribution Networks

Water Distribution Networks (WDNs) frequently experience significant water losses due to pipeline leakages. These losses not only create economic challenges for water utilities but also intensify global concerns regarding water scarcity. This study aims to enhance the accuracy and reliability of leak detection and localisation within WDN infrastructures. Traditional leak detection techniques often exhibit limitations such as high operational costs, inefficient detection processes, and susceptibility to false alarms, particularly when sensors are deployed randomly across the network. Furthermore, detecting concealed or low-intensity leaks remains a difficult task. To address these challenges, this study introduces a hybrid supervised machine learning framework that combines Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Graph Theory (GT). The integration of these techniques enables the proposed model to analyse multiple parameters influencing leak behaviour and improve the reliability of detection outcomes. The hybrid model, referred to as the SVM-ANN-GT algorithm, is evaluated using the EPANET hydraulic simulation environment and compared with conventional machine learning approaches. Experimental results indicate that the proposed hybrid model significantly improves leak detection performance. The model achieves an average detection accuracy of approximately 96%, outperforming standalone SVM and ANN models, which achieved accuracies of 85% and 80%, respectively. The improved performance is primarily attributed to the integration of graph-theoretic optimisation for sensor placement, which enhances monitoring coverage and reduces redundancy within the network.

@article{Komba2026,
  title     = {A Hybrid Machine Learning Algorithm for Pipeline Leak Detection and Localisation in Water Distribution Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Giresse M. Komba and Topside E. Mathonsi and Pius A. Owolawi},
  doi       = {10.14569/IJACSA.2026.0170374},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170374}
}