Genetic algorithm for contaminant source characterization using imperfect sensors

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.