Real-Time Connectivity Modeling of Water Distribution Networks to Predict Contamination Spread

Abstract

This paper presents a new approach to analyzing water distribution networks during a contamination event. Previous computer models for predicting the extent of contamination spread in water distribution networks are demand-driven models. The new approach makes use of supervisory control and data acquisition (SCADA) data to create connectivity matrices, which encapsulate the worst-case projection of the potential spread of contamination obtained by combining the effects of all possible scenarios. Two methods for creating connectivity matrices are described, the first based on operating modes, and the second on fundamental paths. Both methods produce identical results, although the method of fundamental paths is more efficient computationally. The connectivity- and hydraulic-based approaches are compared using an example problem.