A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.
This study describes a decision support system, alerts for contamination events in water distribution systems. The developed model comprises a weighted support vector machine (SVM) for the detection of outliers, and a following sequence analysis for the classification of contamination events. The contribution of this study is an improvement of contamination events detection ability and a multi-dimensional analysis of the data, differing from the parallel one-dimensional analysis conducted so far. The multivariate analysis examines the relationships between water quality parameters and detects changes in their mutual patterns. The weights of the SVM model accomplish two goals: blurring the difference between sizes of the two classes' data sets (as there are much more normal/regular than event time measurements), and adhering the time factor attribute by a time decay coefficient, ascribing higher importance to recent observations when classifying a time step measurement. All model parameters were determined by data driven optimization so the calibration of the model was completely autonomic. The model was trained and tested on a real water distribution system (WDS) data set with randomly simulated events superimposed on the original measurements. The model is prominent in its ability to detect events that were only partly expressed in the data (i.e., affecting only some of the measured parameters). The model showed high accuracy and better detection ability as compared to previous modeling attempts of contamination event detection.
['Oliker|Nurit|N|', 'Ostfeld|Avi|A|']
D003661:Decision Support Techniques D004784:Environmental Monitoring D008956:Models, Chemical D015999:Multivariate Analysis D060388:Support Vector Machine D013997:Time Factors D014874:Water Pollutants, Chemical D060753:Water Quality D014881:Water Supply
Event detection Water distribution systems Support vector machine Sequence analysis Water quality Water security