Water distribution systems flow monitoring and anomalous event detection: A practical approach

Abstract

Methods to detect outliers in network flow measurements that may be due to pipe bursts or unusual consumptions are fundamental to improve water distribution system on-line operation and management, and to ensure reliable historical data for sustainable planning and design of these systems. To detect and classify anomalous events in flow data from district metering areas a four-step methodology was adopted, implemented and tested: i) data acquisition, ii) data validation and normalization, iii) anomalous observation detection, iv) anomalous event detection and characterization. This approach is based on the renewed concept of outlier regions and depends on a reduced number of configuration parameters: the number of past observations, the true positive rate and the false positive rate. Results indicate that this approach is flexible and applicable to the detection of different types of events (e.g., pipe burst, unusual consumption) and to different flow time series (e.g., instantaneous, minimum night flow).