Early Two-Point Leak Localization in Water Distribution Networks Using Topology-Aware Deep Learning

Pipe leakage in water distribution networks causes water loss, pressure decline, energy waste, and reduced service reliability. This study aims to support early localization of two simultaneous pipe leaks using the evaluated 11-sensor pressure-monitoring layout. A topology-aware deep learning framework was proposed on the EPANET 2.0 (Build 2.00.12) Net2 network using WNTR 1.3.2-based hydraulic simulation. Two-point leakage scenarios were generated by pipe-splitting strategy, and a 4 h early-stage pressure-residual window from 11 pressure sensors was used as input. The task was formulated as joint multi-label pipe identification and intra-pipe position regression. A bidirectional long short-term memory (BiLSTM) branch learned the temporal pressure response, while a graph attention network version 2 (GATv2) branch represented sensor–topology relationships. On the validation set, the model achieved a Top-2 F1 score (F1@2) of 61.18%. Both leaking pipes were identified in 31.65% of samples, and at least one true leaking pipe was included in the Top-2 candidates for 90.72% of samples. For correctly matched leak-point instances, the physical mean absolute error was 41.91 m. The results indicate that topology-aware temporal learning can provide pipe-level candidates and intra-pipe inspection distances for early two-point leak localization, although nearby and weak simultaneous leaks remain challenging.