A canonical correlation analysis based method for contamination event detection in water sources

In this study, a general framework integrating a data-driven estimation model is employed for contamination event detection in water sources. Sequential canonical correlation coefficients are updated in the model using multivariate water quality time series. The proposed method utilizes canonical correlation analysis for studying the interplay between two sets of water quality parameters. The model is assessed by precision, recall and F-measure. The proposed method is tested using data from a laboratory contaminant injection experiment. The proposed method could detect a contamination event 1 minute after the introduction of 1.600 mg l(-1) acrylamide solution. With optimized parameter values, the proposed method can correctly detect 97.50% of all contamination events with no false alarms. The robustness of the proposed method can be explained using the Bauer-Fike theorem.