Following the events of September 11, 2001, in the United States, world public awareness for possible terrorist attacks on water supply systems has increased dramatically. Among the different threats for a water distribution system, the most difficult to address is a deliberate chemical or biological contaminant injection, due to both the uncertainty of the type of injected contaminant and its consequences, and the uncertainty of the time and location of the injection. An online contaminant monitoring system is considered as a major opportunity to protect against the impacts of a deliberate contaminant intrusion. However, although optimization models and solution algorithms have been developed for locating sensors, little is known about how these design algorithms compare to the efforts of
A contaminant intentional intrusion into a water distribution system is one of the most difficult threats to address. This is because of the uncertainty of the type of the injected contaminant and its consequences, and the uncertainty of the location and intrusion time. An online contaminant sensor network is the main constituent to enhance the security of a water distribution system against such a threat. In this study a multiobjective model for water distribution system optimal sensor placement using the nondominated sorted genetic algorithm II is developed and demonstrated using two water distribution systems of increasing complexity. Tradeoffs between three objectives are explored: ͑1͒ sensor detection likelihood; ͑2͒ sensor detection redundancy; and ͑3͒ sensor expected detection time. Pareto fronts are plotted for pairs of conflicting objectives, and simultaneously for all three. A contamination event heuristic sampling methodology is developed for overcoming the problem of contamination event sampling.
Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise of thousands to tens of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnection. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Global clustering -a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure -a bottom-up algorithm based on network modularity property, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning -a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a practical decision support tool for water utilities. Visual qualitative and quantitative measures are proposed to evaluate models' performance. The proposed methods are applied and results are evaluated and compared for two large-scale water distribution systems serving heavily populated areas in Singapore.
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