Non-Darwinian evolution for the source detection of atmospheric releases

Guido Cervone, Pasquale Franzese

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A non-Darwinian evolutionary algorithm is presented as search engine to identify the characteristics of a source of atmospheric pollutants, given a set of concentration measurements. The algorithm drives iteratively a forward dispersion model from tentative sources toward the real source. The solutions of non-Darwinian evolution processes are not generated through pseudo-random operators, unlike traditional evolutionary algorithms, but through a reasoning process based on machine learning rule generation and instantiation. The new algorithm is tested with both a synthetic case and with the Prairie Grass field experiment. To further test the capabilities of the algorithm to work in real-world scenarios, the source identification of all Prairie Grass releases was performed with a decreasing number of sensor measurements, and a relationship is found between the precision of the solution, the number of sensors available, and the levels of concentration measured by the sensors.The proposed methodology can be used for a variety of optimization problems, and is particularly suited for problems where the operations needed for evaluating new candidate solutions are computationally expensive.

Original languageEnglish (US)
Pages (from-to)4497-4506
Number of pages10
JournalAtmospheric Environment
Volume45
Issue number26
DOIs
StatePublished - Aug 1 2011

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sensor
prairie
grass
engine
atmospheric pollution
detection
methodology
test
field experiment
machine learning

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Atmospheric Science

Cite this

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abstract = "A non-Darwinian evolutionary algorithm is presented as search engine to identify the characteristics of a source of atmospheric pollutants, given a set of concentration measurements. The algorithm drives iteratively a forward dispersion model from tentative sources toward the real source. The solutions of non-Darwinian evolution processes are not generated through pseudo-random operators, unlike traditional evolutionary algorithms, but through a reasoning process based on machine learning rule generation and instantiation. The new algorithm is tested with both a synthetic case and with the Prairie Grass field experiment. To further test the capabilities of the algorithm to work in real-world scenarios, the source identification of all Prairie Grass releases was performed with a decreasing number of sensor measurements, and a relationship is found between the precision of the solution, the number of sensors available, and the levels of concentration measured by the sensors.The proposed methodology can be used for a variety of optimization problems, and is particularly suited for problems where the operations needed for evaluating new candidate solutions are computationally expensive.",
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Non-Darwinian evolution for the source detection of atmospheric releases. / Cervone, Guido; Franzese, Pasquale.

In: Atmospheric Environment, Vol. 45, No. 26, 01.08.2011, p. 4497-4506.

Research output: Contribution to journalArticle

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