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Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter,multi-site water quality monitoring

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Environmental Science and Ecotechnology 2023年第2期14卷 39-52页

作者： Zilin Li Haixing Liu Chi Zhang Guangtao Fu School of Hydraulic Engineering Dalian University of TechnologyDalianLiaoning116024China Centre for Water Systems University of ExeterExeterEX44QFUK

Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination *** learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains *** study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality *** model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly *** sequential analysis is used to update the likelihood of event occurrence based on the anomaly *** are then generated from the fusion of single-site and multi-site *** proposed method was tested on a WDN for various contamination events with different *** showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination ***,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.

关键词： Contamination detection Generative adversarial network multi-site time series data Water distribution system Water quality

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