A use case of anomaly detection for identifying unusual water consumption in Jordan

Abstract

We present a use case of anomaly detection for identifying the unusual water consumption of consumers. Unusual water consumption may be due to a faulty water meter, fraudulent tampering with a water meter, or a leak in the water pipes within the consumer's property. We apply several anomaly detection methods to a real dataset of 22,877 mechanical water meters located in Amman, the capital city of Jordan. The dataset is unlabeled such that no discrimination is given for any meter whether it records a normal water consumption or not. The objective of this study is to test the hypothesis that abnormal water consumption (registered by a given water meter) can be identified based on previous records of water consumption measured by the same meter. We tested our hypothesis using well-known anomaly detection methods, namely: z-score (zs), local outlier factor (lof), density-based spatial clustering of applications with noise (dbscan), minimum covariance determinant (mcd), one-class support vector machine (ocsvm), and isolation forest (forest). In the settings of our experiments, we observed that zs, lof, ocsvm and forest support our hypothesis, contrasting with dbscan and mcd. HIGHLIGHTS We give a framework for detecting abnormal water consumption.; Our dataset is drawn from the Jordan water supply network.; Our methodology is based on unsupervised machine learning.;

Keywords

• anomaly detection
• water consumption
• water supply network