A Genetic Algorithm Method for Sensor Data Assimilation and Source Characterization

A genetic algorithm is used to couple a dispersion and transport model with a pollution receptor model for the purpose of assimilating sensor data to characterize emission sources. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially...

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Bibliographic Details
Published inThe 2006 IEEE International Joint Conference on Neural Network Proceedings pp. 5096 - 5103
Main Authors Haupt, S.E., Allen, C.T., Young, G.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Calibration
Data assimilation
Genetic algorithms
Meteorology
Monte Carlo methods
Noise robustness
Pollution
Sensor phenomena and characterization
Testing
White noise
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Summary:A genetic algorithm is used to couple a dispersion and transport model with a pollution receptor model for the purpose of assimilating sensor data to characterize emission sources. This coupling allows the use of the backward (receptor) model to calibrate the forward (dispersion) model, potentially across a wide range of meteorological conditions. The genetic algorithm optimizes the source calibration factors that connect the two models. This methodology is demonstrated for a basic Gaussian plume dispersion model, then progresses to incorporating an operational transport and dispersion model. It is 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. The impact of varying the genetic algorithm parameters is assessed.
ISBN:9780780394902
0780394909
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2006.247238