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A simple, straightforward, modified genetic algorithm scheme for contaminant source characterization using imperfect sensors is presented and demonstrated in this study. Previous work on this subject concentrated on developing source-inversion models using sensors that provide accurate, unbiased, contamination concentration measurements. The developed contamination source-detection model is implemented using three sensor types: (1) perfect sensors providing accurate, unbiased, contamination concentration measurements; (2) sensors transmitting fuzzy measured information (i.e., high, medium, and low contamination); and (3) '0-1' (Boolean) sensors indicating only a contamination presence. A comparison between the three sensor types is explored taking into consideration thesystem's response time (i.e., the time elapsed between a contaminant detection and a decision-maker's response action). The methodology capabilities are demonstrated using two example applications of increasing complexity, showing the trade-offs between the sensor types and the model abilities to receive a unique solution to the source-detection problem. [ABSTRACT FROM AUTHOR]

Drinking water utilities around the world are vulnerable to various types of terrorist attacks including warfare contamination and bioterrorism. A distribution system comprises water tanks, pipes, pumps, and other components that deliver treated water from treatment plants to consumers. Particularly among large utilities, distribution systems may contain thousands of kilometers of pipes and numerous delivery points, which can be highly vulnerable to a terrorist deliberate contamination injection. This paper extends previous work on optimal early warning monitoring system layout for water networks security by addressing the monitoring stations detection sensitivities and response delays, and the consumer demands and contaminant injected flow rates randomness. The methodology developed is demonstrated on two example applications. [ABSTRACT FROM AUTHOR]