TY - GEN
T1 - A genetic algorithm method to assimilate sensor data for homeland defense applications
AU - Haupt, Sue Ellen
AU - Allen, Christopher T.
AU - Young, George S.
PY - 2006
Y1 - 2006
N2 - A critical problem in homeland defense is correctly characterizing the source of hazardous material. Field monitors are expected to measure concentrations of toxic material. Algorithms are then required that back-calculate the parameters of the source and the local meteorology so that subsequent predictive modeling can inform decision-makers. Here, a genetic algorithm is used together with transport and dispersion models to assimilate sensor data to characterize emission sources. The parameters computed include location, time, and amount of the release and meteorological conditions relevant to the transport and dispersion. This methodology is demonstrated for a basic Gaussian plume dispersion model and verified in the context of both synthetic data and actual monitored data from field tests with known release amounts. Its error bounds are set using Monte Carlo techniques and robustness assessed through the addition of white noise. Algorithm speed is tuned through optimizing the parameters of the genetic algorithm.
AB - A critical problem in homeland defense is correctly characterizing the source of hazardous material. Field monitors are expected to measure concentrations of toxic material. Algorithms are then required that back-calculate the parameters of the source and the local meteorology so that subsequent predictive modeling can inform decision-makers. Here, a genetic algorithm is used together with transport and dispersion models to assimilate sensor data to characterize emission sources. The parameters computed include location, time, and amount of the release and meteorological conditions relevant to the transport and dispersion. This methodology is demonstrated for a basic Gaussian plume dispersion model and verified in the context of both synthetic data and actual monitored data from field tests with known release amounts. Its error bounds are set using Monte Carlo techniques and robustness assessed through the addition of white noise. Algorithm speed is tuned through optimizing the parameters of the genetic algorithm.
UR - http://www.scopus.com/inward/record.url?scp=34250723319&partnerID=8YFLogxK
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U2 - 10.1109/SMCALS.2006.250723
DO - 10.1109/SMCALS.2006.250723
M3 - Conference contribution
AN - SCOPUS:34250723319
SN - 1424401666
SN - 9781424401666
T3 - 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
SP - 243
EP - 248
BT - 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
T2 - 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
Y2 - 24 July 2006 through 26 July 2006
ER -