Contamination source identification in water distribution networks using convolutional neural network

Contamination source identification (CSI) is significant for water quality security and social stability when a contamination intrusion event occurs in water distribution systems (WDSs). However, in research, this is an extremely challenging task for many reasons, such as limited number of water quality sensors and their limitations in detecting contaminants. Hence, some researchers have introduced consumers' complaint information as an alternative of sensors for CSI. But the problem with this approach is that the uncertainty of complaint delay time has a great impact on the identification accuracy. To address this issue, this study constructed complaint matrices to present the spatiotemporal characteristics of consumer complaints in an intrusion event and proposed a new methodology employing convolution neural network (CNN)-a deep learning algorithm-for the purpose of pattern recognition. CNN aimed to explore the inherent characteristics of complaint patterns corresponding to different contaminant intrusion nodes and to improve the performance of identifying the contamination source based on consumer complaint information. Two case studies illustrated methodology effectiveness in WDSs of various scales, even with the high uncertainties of complaint delay time. The comparison between CNN and a back-propagation artificial neural network algorithm demonstrates that the former framework possesses stronger robustness and higher accuracy for CSI.