A genetic algorithm method to assimilate sensor data for homeland defense applications

Sue Ellen Haupt, Christopher T. Allen, George Spencer Young

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publication2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
Pages243-248
Number of pages6
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006 - Logan, Utah, United States
Duration: Jul 24 2006Jul 26 2006

Publication series

Name2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006

Other

Other2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006
CountryUnited States
CityLogan, Utah
Period7/24/067/26/06

Fingerprint

homeland defense
Genetic algorithms
Genetic Algorithm
Sensor
Sensors
Concentration of Measure
Hazardous Materials
Toxic materials
Predictive Modeling
Meteorology
Hazardous materials
Monte Carlo Techniques
White noise
Synthetic Data
hazardous material
meteorology
Error Bounds
Monitor
Robustness
decision maker

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Education

Cite this

Haupt, S. E., Allen, C. T., & Young, G. S. (2006). A genetic algorithm method to assimilate sensor data for homeland defense applications. In 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006 (pp. 243-248). [4016794] (2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006). https://doi.org/10.1109/SMCALS.2006.250723
Haupt, Sue Ellen ; Allen, Christopher T. ; Young, George Spencer. / A genetic algorithm method to assimilate sensor data for homeland defense applications. 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006. 2006. pp. 243-248 (2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006).
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Haupt, SE, Allen, CT & Young, GS 2006, A genetic algorithm method to assimilate sensor data for homeland defense applications. in 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006., 4016794, 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006, pp. 243-248, 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006, Logan, Utah, United States, 7/24/06. https://doi.org/10.1109/SMCALS.2006.250723

A genetic algorithm method to assimilate sensor data for homeland defense applications. / Haupt, Sue Ellen; Allen, Christopher T.; Young, George Spencer.

2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006. 2006. p. 243-248 4016794 (2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Haupt SE, Allen CT, Young GS. A genetic algorithm method to assimilate sensor data for homeland defense applications. In 2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006. 2006. p. 243-248. 4016794. (2006 IEEE Mountain Workshop on Adaptive and Learning Systems, SMCals 2006). https://doi.org/10.1109/SMCALS.2006.250723