Abstract:
Leakage within Water Distribution Networks (WDNs) constitutes a primary concern for utilities, significantly impacting water conservation and the ability to meet consumer demand. In addressing this pervasive issue, the present study explores the efficacy of a comprehensive detection and localization strategy across District Meter Areas (DMAs). The research hinges on the development of a calibrated hydraulic model that adheres to stringent standards, minimizing error margins and setting a precedent in the domain of water network analysis. Central to the study is the implementation of an autoencoder-based deep learning framework, which has demonstrated proficiency in the detection of leaks. To transcend the limitations imposed by sparse sensor networks, a novel hybrid approach was adopted, integrating the hydraulic model with data-driven algorithms to enhance the localization of leaks. The methodology involved segmenting the DMA into subzones and applying rolling window simulations to create a diverse dataset for training a Convolutional Neural Network (CNN). The standout performance of the model, particularly with a 6-hour rolling window, was corroborated by its ability to precisely localize leaks in an actual case scenario. Addressing this issue, our research advances a strategy for leak detection and localization that is both economical and effective, utilizing minimal sensor input and also the importance of additional sensors to localize leak even more accurately. The implications of this research extend towards reinforcing the operational integrity of WDNs, fostering water conservation, and ensuring resource availability and sustainability in the face of escalating demand.
Keywords: Water Distribution Networks, Hydraulic Modelling, Data-Driven Methods, Spectral Clustering, Leakage Detection, Leakage Localization.
This thesis is part of the “UtilityTwin” project.