Evolutionary Algorithm and Expectation Maximization Strategies for Improved Detection of Pipe Bursts and Other Events in Water Distribution Systems

Abstract

A fully automated data-driven methodology for the detection of pipe bursts and other events that induce similar abnormal pressure/flow variations (e.g., unauthorized consumptions) at the district metered area (DMA) level has been recently developed by the authors. This methodology works by simultaneously analyzing the data coming on-line from all the pressure and/or flow sensors deployed in a DMA. It makes synergistic use of several self-learning artificial intelligence (AI) and statistical techniques. These include (1) wavelets for the de-noising of the recorded pressure/flow signals; (2) artificial neural networks (ANNs) for the short-term forecasting of pressure/flow signal values; (3) statistical process control (SPC) techniques for the short-term and long-term analysis of the burst/other event-induced pressure/flow variations; and (4) Bayesian inference systems (BISs) for inferring the probability that a pipe burst/other event has occurred in the DMA being studied, raising the corresponding detection alarms, and provide information useful for performing event diagnosis. This paper focuses on the (re)calibration of the above detection methodology with the aim of improving the forecasting performances of the ANN models and the classification performances of the BIS used to raise the detection alarms (i.e., DMA-level BIS). This is achieved by using (1) an Evolutionary Algorithm optimization strategy for selecting the best ANN input structures and related parameter values to be used for training the ANN models, and (2) an Expectation Maximization strategy for (re)calibrating the values in the conditional probability tables (CPTs) of the DMA-level BIS. The (re)calibration procedure is tested on a case study involving several DMAs in the U.K. with real-life pipe bursts/other events, engineered pipe burst events (i.e., simulated by opening fire hydrants), and synthetic pipe burst events (i.e., simulated by arbitrarily adding “burst flows” to an actual flow signal). The results obtained illustrate that the new (re)calibration procedure improves the performance of the event detection methodology in terms of increased detection speed and reliability.