Combining clustering and regularised neural network for burst detection and localization and flow/pressure sensor placement in water distribution networks

This paper proposes a novel methodology for simultaneously addressing design of burst detection/localization machine learning algorithm and flow/pressure sensor placement in water distribution networks (WDNs). A preliminary spectral clustering is performed for defining a suitable WDN partitioning, in order to carry out burst localization at cluster level and reduce the computational burden. A grouped regularization approach is used to define the most suitable flow and pressure sensor placement, inter and intra clusters, respectively, and train a neural network classification model, while explicitly considering sensor cost and data redundancy. Several burst (one at a time, at any WDN node) and water demand scenarios are simulated, for a time window of 30 days, for accounting spatial/temporal uncertainties of WDN hydraulic behaviour. The methodology is applied to a real-world WDN in Italy, pre-clustered in four detection areas, showing outstanding detection/localization test accuracy for small (80 % / on average 86.55 %), and especially moderate (100 % / on average 98.43 %) to large (100 % / on average 96.18 %) burst entities, even when relying on a very reduced number of sensors (just five flow meters located on the boundary pipes between detection areas). •Machine learning methodology for burst detection/localization and sensor placement•Workload and granularity of burst localization balanced using spectral clustering•Variability in demand patterns, burst entity and location explicitly considered•Sensor cost and data redundancy handled using group regularization•High detection/localization accuracy for moderate/large bursts using few sensors