Spatiotemporal graph learning for detection and localization of external inflows in sustainable urban sewer systems.

Urban sewer systems increasingly require intelligent, low-cost monitoring strategies to maintain environmental safety and operational resilience. This study proposes a spatiotemporal graph learning framework for detection and localization of external inflows in urban sewer systems. The framework combines three inputs: (i) the inflow-affected water-level time series from one downstream sensor, (ii) simulated baseline water-level series for all upstream nodes generated using EPA SWMM, and (iii) a weighted graph representation of network topology. An LSTM-GCN regression module first estimates the event-level maximum water-level deviation at each node rather than the full deviation time series, improving robustness to unknown inflow hydrographs. A downstream GCN classifier then converts the inferred spatial response into branch-level probabilities for source localization. The method was evaluated in a synthetic 117-junction test network and a 39-junction real-world sewer subnetwork. In the synthetic single-source case, the true source branch was ranked within the top two candidates in over 90% of test events. In the real-world case, the Top1 branch localization reached 100%. Sensitivity analyses further showed that larger inflow magnitudes and hydraulically coherent branch partitioning improved localization reliability. These results demonstrate the potential of graph-based single-sensor diagnostics for low-cost sewer monitoring and targeted inspection planning.