Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs-Reference-Cited by-同舟云学术

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Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs

Published:2016-02-18 Issue:11 Volume:23 Page:10753-10762
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Stamenković Lidija J.,Antanasijević Davor Z.,Ristić Mirjana Đ.,Perić-Grujić Aleksandra A.,Pocajt Viktor V.

Funder

Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

http://link.springer.com/content/pdf/10.1007/s11356-016-6279-z.pdf

Reference43 articles.

1. Alonso JM, Alvarruiz F, Desantes JM, Hernández L, Hernández V, Moltó G (2007) Combining neural networks and genetic algorithms to predict and reduce diesel engine emissions. IEEE T Evolut Comput 11:46–55

2. Alves da Silva AP, Ferreira VH, Velasquez RMG (2008) Input space to neural network based load forecasters. Int J Forecast 24:616–629

3. American Chemistry Council (2015) Chemical Shipments http://www.americanchemistry.com/ . Accessed 21 April 2015

4. Antanasijević DZ, Pocajt VV, Povrenović DS et al (2013) PM 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519

5. Antanasijević DZ, Ristić MĐ, Perić-Grujić AA, Pocajt VV (2014) Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. Int J Greenh Gas Control 20:244–253

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