ID:
publications-3968
Type:
article
Year:
2012
Authors:
Perelman, Lina and Arad, Jonathan and Housh, Mashor and Ostfeld, Avi
Title:
Event detection in water distribution systems from multivariate water quality time series.
Venue/Journal:
Environmental Science & Technology
DOI:
10.1021/es3014024
Research type:
Water System:
Technical Focus:
Abstract:
In this study, a general framework integrating a data-driven estimation model with sequential probability updating is suggested for detecting quality faults in water distribution systems from multivariate water quality time series. The method utilizes artificial neural networks (ANNs) for studying the interplay between multivariate water quality parameters and detecting possible outliers. The analysis is followed by updating the probability of an event, initially assumed rare, by recursively applying Bayes’ rule. The model is assessed through correlation coefficient (R2), mean squared error (MSE), confusion matrices, receiver operating characteristic (ROC) curves, and true and false positive rates (TPR and FPR). The product of the suggested methodology consists of alarms indicating a possible contamination event based on single and multiple water quality parameters. The methodology was developed and tested on real data attained from a water utility.
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