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Artificial Neural Networks for Estimating the Atmospheric Pollutant Sources

In: Integral Methods in Science and Engineering

Author

Listed:
  • F. F. Paes

    (Instituto Nacional de Pesquisas Espaciais (INPE))

  • H. F. de Campos Velho

    (Instituto Nacional de Pesquisas Espaciais (INPE))

  • F. M. Ramos

    (Instituto Nacional de Pesquisas Espaciais (INPE))

Abstract

The increasing concentration of greenhouse effect gases is a central issue nowadays, mainly with regard to the anthropogenic production gases, such as methane (CH4) and carbon dioxide (CO2). Despite the ratification of the Kyoto Protocol, the expectation is the releases of CO2 and CH4 into the atmosphere will continue to increase in next decade (IPCC, 2007). One essential strategy is to monitor the concentration of these gases in the atmosphere. However, in order to understand the bio-geochemical cycle of these gases, it is necessary to estimate the surface emission rates. One procedure to do that is to employ an inverse problem methodology. Here, the artificial neural network is employed to compute the inverse solution with good results.

Suggested Citation

  • F. F. Paes & H. F. de Campos Velho & F. M. Ramos, 2011. "Artificial Neural Networks for Estimating the Atmospheric Pollutant Sources," Springer Books, in: Christian Constanda & Paul J. Harris (ed.), Integral Methods in Science and Engineering, edition 1, pages 261-271, Springer.
    • Handle: RePEc:spr:sprchp:978-0-8176-8238-5_25
    DOI: 10.1007/978-0-8176-8238-5_25
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