Journal of Pattern Recognition Research

In this paper we investigated and analyzed the concept of virtual DMA as the core objective of the research to resolve the current Gap and limitations of the DMA state of practice through the development of Virtual DMA Leakage Monitoring and Classification System Using Multi-class Support Vector Machine (SVM) Advanced Pattern Recognizer at Lille University WDS study area the so called “Zone-6”. The SVM’s were trained on multiple cases representing the presence of leakages in various sizes and locations. The research results, and analysis showed a rather promising performance, which could be successfully implemented for leak detection and classification. The proposed method could enable the water Utility companies and other stakeholders to further reduce risks associated with pipeline leakage or breakage. This method can be used during decision-making process for selecting which WDS infrastructure required urgent action, and engineer the optimal short-term response or alternative rehabilitation and replacement (R&R) Maintenance Strategies. Furthermore, the proposed methodology could benefit the water utility companies by reducing the cost and operational drawbacks associated with implementing physical DMA. It also improve the day to day operational decision making process by detecting and classifying the different stages of pipelines leakages and  breakages according to their severity, which can help the operators to see the  behavior of the network on the control room screens they are familiar with and enable them to quickly perform the best short-term response strategy.