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This paper explores two applied classification models alerting for contamination events in water distribution systems. The models perform multivariate analysis of water quality online measurements for event detection. The developed models comprise an outlier detection algorithm and a following sequence analysis for the classification of events. The first model is an unsupervised minimum volume ellipsoid (MVE), which utilizes only normal operation measurements but requires calibration. The second is a supervised weighted support vector machine, which utilizes event examples and performs data-driven optimized calibration. The models were trained and tested on real water utility data with randomly simulated events that were superimposed on the original database. The models showed high accuracy and detection ability compared to previous studies. All in all, the MVE model achieved preferable results. [ABSTRACT FROM AUTHOR]

This study presents a new method for selecting monitoring wells for optimal evaluation of groundwater quality. The basic approach of this work is motivated by difficulties in interpolating groundwater quality from information collected for only few sampled wells. The well selection relies on other existing data relevant to contaminant distribution in the sampling domain, e.g. predictions of models which rely on past measurements. The objective of this study is to develop a method of selecting the optimal wells, from which measurements could best serve some external model, e.g. a kriging system for characterizing the entire plume distribution, a flow-and-transport model for predicting a future distribution, or an inverse model for locating contaminant sources or estimating aquifer parameters. The decision variable at each sampling round determines the specific wells to be sampled. The study objective is accomplished through a spatially-continuous utility density function (UDF) which describes the utility of sampling at every point. The entire methodology which utilizes the UDF in conjunction with a sampling algorithm is entitled the UDF method. By applying calculations in steady and unsteady state sampling domains the effectiveness of the UDF method is demonstrated. [ABSTRACT FROM AUTHOR]