Detection of Anomalies in Water Networks by Functional Data Analysis

Abstract

A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of anomalies to ensure that data are representative of the in-control system. The objective of Phase II is system monitoring. A new functional outlier detection method is also proposed based on archetypal analysis. The methodology is applied and illustrated with real data. A simulated study is also carried out to assess the performance of the outlier detection techniques, including our proposal. The results are very promising.