Flow Data Monitoring in Water Distribution Networks Using Density-Based Clustering Methods

Anomaly or outlier detection of flow data in water distribution networks (WDNs) is implemented in data preparation and prepossessing step to achieve reliable historical data; it is important to improve the leakage assessment and management methods and the operations of the network efficiently. The main objective of this paper is to develop a new methodology based on unsupervised learning methods for anomaly or outlier detection in a flow data set in WDNs. The developed methodology includes three steps 1- required data acquisition, 2- data validation and normalization, and 3- anomaly or outlier detection using the density-based spatial clustering of application with noise (DBSCAN) algorithm. The proposed methodology is applied for inflow data into an area in Tehran's urban water distribution network with 15-min sampling intervals for 1394. The results showed that the developed methodology is capable to the detection anomalies due to different type of pipe breaks and unusual legitimate consumption such as water usage due to changes in water consumption pattern or unauthorized consumption. Therefore, this methodology can be used as an applicable and flexible tool for monitoring flow data and detecting and eliminating of different types of outliers from them.