title: Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach.
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%.
authors.affiliation: Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11120, Belgrade, Serbia.
authors.forename: Lidija J
authors.initials: LJ
authors.lastname: Stamenković
authors.affiliation: Innovation Center of the Faculty of Technology and Metallurgy, Karnegijeva 4, 11120, Belgrade, Serbia. dantanasijevic@tmf.bg.ac.rs.
authors.forename: Davor Z
authors.initials: DZ
authors.lastname: Antanasijević
authors.affiliation: Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11120, Belgrade, Serbia.
authors.forename: Mirjana Đ
authors.initials: MĐ
authors.lastname: Ristić
authors.affiliation: Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11120, Belgrade, Serbia.
authors.forename: Aleksandra A
authors.initials: AA
authors.lastname: Perić-Grujić
authors.affiliation: Faculty of Technology and Metallurgy, University of Belgrade, Karnegijeva 4, 11120, Belgrade, Serbia.
authors.forename: Viktor V
authors.initials: VV
authors.lastname: Pocajt
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000641:Ammonia / Q000032:analysis*; D005060:Europe; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D014481:United States
chemical_list: D000393:Air Pollutants; D000641:Ammonia
keywords: ANN; Ammonia emissions; MLP; National emissions; PCA