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Abstract: The presented study features an event detection model alerting for contamination events in water distribution systems. The developed model comprises a minimum volume ellipsoid (MVE) classifier, detecting outlier measurements, and a following sequence analysis utilizing the MVE binary output, for the classification of events. The model is updated continuously and exploits a constantly growing data base. The MVE enables simultaneous analysis of the water quality parameters. The multivariate analysis explores the relations between water quality parameters and detects changes in their common patterns. The suggested model applied an un-supervised classification method, eliminates the need for simulated events examples in the classifier construction. In the absent of satisfying information regarding the influence of contamination event on the parameter measurements, eliminating the use of any assumption contributes to the model reliability and generality. The model was trained on a real water utility data, and tested on randomly simulated events that were superimposed on the original data base. The model showed high accuracy and detection ability compared to previous studies. [Copyright &y& Elsevier]

This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach. [ABSTRACT FROM AUTHOR]