ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

A Genetic Algorithm Method to Assimilate Sensor Data for a Toxic Contaminant Release
Sue Ellen Haupt, George S. Young, and Christopher T. Allen
Page(s): 85-93
Full Text:
PDF (571 KB)

Following a toxic contaminant release, either accidental or intentional, predicting the transport and
dispersion of the contaminant becomes a critical problem for Homeland Defense and DoD
agencies. To produce accurate predictions requires characterizing both the source of hazardous
material and the local meteorological conditions. Decision makers use information on contaminant
source location and transport prediction to decide on the best methods to mitigate and prevent
effects. The problem has both observational and computational aspects. Field monitors are likely to
be used to detect the release and measure concentrations of the toxic material. Algorithms are then
required to invert the problem in order to infer the characteristics of the source and the local
meteorology. Here, a genetic algorithm is coupled with transport and dispersion models to
assimilate sensor data in order to characterize emission sources and the wind vector. The
parameters computed include two dimensional source location, amount of the release, and wind
direction. This methodology is demonstrated for a basic Gaussian plume dispersion model and  
verified in the context of an identical twin numerical experiment, in which synthetic dispersion data is
created with the same dispersion model. Error bounds are set using Monte Carlo techniques and
robustness assessed by adding white noise. Algorithm speed is tuned by optimizing the
parameters of the genetic algorithm. Specific GA configurations and cost function formulations are

Index Terms
source inversion, genetic algorithm, data assimilation, sensor data fusion