Districting Methods for Water Distribution Networks

Growing awareness of environmental issues and the need for sustainable resource management increasingly underscore the importance of water as a finite and valuable resource. Despite this recognition, many water distribution systems suffer from significant inefficiencies, including excessive leakage, limited preventative measures, and a predominately reactive maintenance approach that often leaves chronic leaks unresolved. These challenges are exacerbated by rising water demand, aging infrastructure, and minimal digital oversight, all of which complicate leak detection and localization. To address these issues, this study introduces methods for clustering system components into geographic districts based on similar leakage behaviors. This “districting” strategy aims to facilitate targeted maintenance by highlighting the most critical areas of the network. We compare our methods with established clustering techniques - such as K-Means, Spectral Clustering, and graph-based algorithms - and explore the impact of the different hyperparameters. Experiments conducted on a modeled urban dataset from the BattLeDIM project demonstrate the effectiveness of our proposed approach in enhancing network segmentation and optimizing leak management. The presented results offer valuable insights for improving the overall performance of water distribution systems.