A new methodology was proposed for contamination source identification using information provided by consumer complaints from a probabilistic view.Due to the high uncertainties of information derived from users,the objective of the proposed methodology doesn't aim to capture a unique solution,but to minimize the number of possible contamination sources.In the proposed methodology,all the possible pollution nodes are identified through the CSA methodology firstly.And then based on the principle of total probability formula,the probability of each possible contamination node is obtained through a series of calculation.According to magnitude of the probability,the number of possible pollution nodes is minimized.The effectiveness and feasibility of the methodology is demonstrated through an application to a real case of ZJ City.Four scenarios were designed to investigate the influence of different uncertainties on the results in this case.The results show that pollutant concentration,injection duration,the number of consumer complaints nodes used for calculation and the prior probability with which consumers would complaint have no particular effect on the identification of contamination source.Three nodes were selected as the most possible pollution sources in water pipe network of ZJ City which includes more than 3 000 nodes.The results show the potential of the proposed method to identify contamination source through consumer complaints.
An intrusion of contaminants into the water distribution network (WDN) can occur through storage tanks (via animals, dust-carrying bacteria, and infiltration) and pipes. A sensor network could yield useful observations that help identify the location of the source, the strength, the time of occurrence, and the duration of contamination. This paper proposes a methodology for identifying the contamination sources in a water distribution system, which identifies the key characteristics of contamination, such as location, starting time, and injection rates at different time intervals. Based on simplified hypotheses and associated with a high computational efficiency, the methodology is designed to be a simple and easy-to-use tool for water companies to ensure rapid identification of the contamination sources, The proposed methodology identifies the characteristics of pollution sources by matching the dynamic patterns of the simulated and measured concentrations. The application of this methodology to a literature network and a real WDN are illustrated with the aid of an example. The results showed that if contaminants are transported from the sources to the sensors at intervals, then this method can identify the most possible ones from candidate pollution sources. However, if the contamination data is minimal, a greater number of redundant contamination source nodes will be present. Consequently, more data from different sensors obtained through network monitoring are required to effectively use this method for locating multi-sources of contamination in the WDN.
