NDLI: Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach

Content Provider	Springer Nature Link
Author	Stamenković, Lidija J. Antanasijević, Davor Z. Ristić, Mirjana Đ. Perić Grujić, Aleksandra A. Pocajt, Viktor V.
Copyright Year	2015
Abstract	Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20 %.
Starting Page	18849
Ending Page	18858
Page Count	10
File Format	PDF
ISSN	09441344
Journal	Environmental Science and Pollution Research
Volume Number	22
Issue Number	23
e-ISSN	16147499
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-07-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	ANN MLP PCA National emissions Ammonia emissions Environment Environmental Chemistry Ecotoxicology Environmental Health Atmospheric Protection/Air Quality Control/Air Pollution Waste Water Technology Water Pollution Control Water Management Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Health, Toxicology and Mutagenesis Pollution Medicine

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