Scheduling Pressure Reducing Valves for Reducing Water-Leakage in Urban Networks Using K-Means Algorithm - Durmuş, Ali; Karaköse, Ercan; Kurban, Rifat; Kütük, Fatih (2025)
Water losses are a vital problem to sustainable water management in urban water distribution networks. This study presents an approach using K-Means clustering algorithm to reduce water losses and leakages through programming of pressure reducing valves (PRVs). The research uses 24-hour water network pressure and flowrate data obtained from a supervisory control and data acquisition (SCADA) system monitoring a district metered area (DMA) located in a metropolitan municipality in the Central Anatolia Region of Türkiye. The proposed methodology divides the temporal flowrate data into 12 different time periods. This is achieved by applying K means clustering to a dataset that maps the flow data to the time of day. Then, the optimal pressure setting values are determined for each time period by mapping the lowest and highest flow values at the obtained cluster centers to the user-defined minimum and maximum desired network operating pressures. Simulation studies were performed using the K-means algorithm both the squared euclidean (dSE) and city block distance (dCB) metrics over 50 runs. Performance was evaluated against an error metric that combines the flow rate and hourly mean absolute error. The results revealed that the dCB distance metric outperformed the dSE metric in terms of mean and best error values. Clustering successfully identified distinct daily flow patterns and provided a dynamic pressure management strategy: lower pressures (e.g. 3.0 bar) were assigned during low demand periods, while higher pressures (up to 5.0 bar) were set during peak consumption times. This data-driven PRV scheduling significantly demonstrates the potential to reduce water leakage and optimize energy consumption in water distribution systems, thus increasing operational efficiency and sustainability while ensuring adequate service levels.
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