The generator is trained to map historical image data to expected current images, while the discriminator differentiates between generated and actual normal images. That method is benchmarked against a multivariate unsupervised method using a minimum-volume-ellipsoid (MVE)-based event detection model . That method demonstrates superior performance in all contamination scenarios, including enhanced detection rates and reduced false alarms, particularly for sensor groups positioned at varying distances from the contamination source. Another unique ML approach can rapidly signal potential contamination risks in drinking water . That approach uses an algorithm for the early detection of drinking water contamination against an unpredictable stochastic background. By extracting key features from the spectrophotometric characteristics of water, the algorithm can effectively identify contamination using a unique affinity measure . The measure compares the absorbance spectra of different water sources, thereby amplifying the feature dissimilarity between portable and contaminated water, followed by processing via SVM and post-processing. That chain of data processing generates a reliable early warning system for contamination events with low false positives and high true alarm accuracy. The pre-processing stage (the affinity measure and amplification) is essential to achieving high accuracy but may be unnecessary to obtain minimal false positives.