However, these models focus on single-site, one-dimensional time series data, neglecting the spatial relationships inherent in multi-site sensor data. This limitation could increase false alarm rates, particularly under conditions of high hydraulic variability. To address this issue, a follow-up study proposed a novel unsupervised, generative-adversarial-networks-based (GAN-based) multivariate method to detect multi-site contamination events (Figure ) . That method effectively captures spatiotemporal patterns by transforming water quality data from single and multiple sites into superimposed images. The GAN-based model, having a generator and a discriminator, evaluates the degree of abnormality at each time step by generating anomaly scores.