Application of machine learning techniques to predict anomalies in water supply networks
Methods to improve the operational efficiency of a water supply network by early detection of anomalies are investigated by making use of the data streams from multiple sensor locations within the network. The water supply network is a demonstration site of Vitens, a Dutch water company that has several district metering areas where flow, pressure, electrical conductance and temperature are measured and logged online. Three different machine learning approaches are tested for their feasibility to detect anomalies. In the first approach, day-dependent support vector regression (SVR) models are trained for predicting the measurement signals and compared to straightforward models using mean and median estimates, respectively. Using SVRs or the averaged data as real-time pattern recognizers on all available signals, large leakages can be detected. The second approach utilizes adaptive orthogonal projections and reports an event when the number of hidden variables required to describe the streaming data to a user-defined degree (energy-level threshold) increases. As a third approach, (unsupervised) clustering techniques are applied to detect anomalies and underlying patterns from the raw data streams. Preliminary results indicate that the current dataset is too limited in the amount of events and patterns to harness the potential of these techniques.