Anomaly Detection at the DMA-Level via Isolation Forest

This study applies the Isolation Forest (IF) algorithm to detect anomalies in a district metered area (DMA) in Emilia-Romagna, Italy. Multiple datasets are analyzed, including 15-min inflows, daily minima, and inflows excluding the consumption of a high-demand industrial user. Anomalies are cross-referenced with repair records to assess correlation with leaks and failures and a metric is defined to evaluate the algorithm performance across datasets. Results show that sensor malfunctions and communication anomalies can be effectively detected through the application of the IF algorithm. Regarding the detection of burst and leakage events, the automated analysis of daily minima is the most effective and removing industrial consumption enhances detection accuracy.