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 (R(2)), 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.

No abstract

No abstract

No abstract

Detecting contamination events in water supply systems is a constant concern for utilities. It is reasonable to assume that injection of foreign substances will affect the behaviour of typically measured water parameters. For this reason, identifying contaminants using water quality and hydraulic measurements which are regularly monitored is appealing. A generic framework integrating Decision Trees (DTs) and Bayesian sequential probability updating rule is presented for detecting contamination events in Water Distribution Systems (WDS). The Aquatic Event Detection Algorithm (AEDA) utilizes DTs to depict the correlation between water quality and hydraulic parameters in order to detect possible outliers. The analysis is followed by updating the probability of a contamination event by recursively applying Bayes rule. AEDA is assessed through correlation coefficient (R 2), Mean Squared Error (MSE), confusion matrices, Receiver Operating Characteristic (ROC) curves, and True and False Positive Rates (TPR and FPR). AEDA is tested using simulated contamination events, imposed on water parameters, to imitate pollution scenarios in WDS.