Artificial Neural Networks for Estimating the Atmospheric Pollutant Sources

F. F. Paes (), H. F. de Campos Velho () and F. M. Ramos ()
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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)

A chapter in Integral Methods in Science and Engineering, 2011, pp 261-271 from Springer

Abstract: 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.

Keywords: Inverse Problem; Particle Swarm Optimization; Hide Layer; Neuron Hide Layer; Lagrangian Stochastic Model (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-8176-8238-5_25

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DOI: 10.1007/978-0-8176-8238-5_25

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