biostudies-literatureTan DYNatural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)National Natural Science Foundation of China (National Science Foundation of China)156https://www.ebi.ac.uk/biostudies/studies/S-EPMC12357945biostudies-literatureUnknown4(1)Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-basedCommunications engineeringReal-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals.PMC1235794542225702BK2024121142407250Zhang WHuang JWWang PTan DYShi BTang ZYDuan HFYan ZRZhu HHWang JYuan ZfalseReal-time monitoring of water states in large-diameter aqueducts - learning from distributed acoustic sensing signals.Large-diameter gravity aqueducts are essential for water supply systems but face performance and safety risks from complex flow conditions. Effective flow-state monitoring is critical for hydraulic performance and infrastructure safety. However, conventional monitoring techniques like closed-circuit television (CCTV) inspection and ultrasonic sensing have limited real-time accuracy in distinguishing flow states. Here we show a real-time, distributed flow monitoring framework based on distributed acoustic sensing (DAS). A hierarchical clustering model, called DAS-Hydro HierarchyNet, was developed to analyze low-frequency acoustic signals and classify water flow states using a multi-level approach. The framework enables continuous flow monitoring along large aqueducts, overcoming point-based2025-01-01T00:00:00Z2025 Aug2026-04-13T17:11:33.107Z2026-04-07T13:34:25.842ZS-EPMC123579454081918510.1038/s44172-025-00483-6