Real-Time Identification of Possible Contamination Sources Using Network Backtracking Methods

Abstract

In case of contamination intrusion in water distribution systems, water quality sensor data can be used to determine the location and time of the contamination source. One approach to contamination source identification is finding the source location that minimizes the difference between modeled and measured water quality. However, this is an inherently ill-posed mathematical problem, due to the shortage of measurements compared to source parameters, and regularization methods are required to force identification of a unique solution. An alternative practical method is developed in this paper to identify all possible locations and times that explain contamination incidents detected by the water quality sensors. Since sensors cannot detect the quantitative concentration of a contaminant, this method only requires a binary sensor status over time. A particle backtracking algorithm is used to identify the water flow paths and travel times leading to each sensor measurement. Those locations and times that are connected to positive sensor measurements, but are not connected to negative measurements, are the possible sources, assuming no false positive/negative readings and an accurate hydraulic model. The method also forms the basis for incorporating important concerns about hydraulic and sensor uncertainty, which are likely to enlarge the set of possible sources.