TY  - JOUR
AU  - Stamenković, Lidija J.
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2017
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3610
AB  - Nitrogen oxides (NOx) emissions into the atmosphere have multiple negative effects on the environment and effects directly and indirectly on human health. This paper describes the development of a model for NO (x) emission prediction at the national level based on artificial neural networks (ANNs) and on widely available sustainability, industrial, and economical parameters as input variables. In this study, 11 sustainability, industrial, and economical parameters were chosen as potential input variables. The ANN models were trained, validated, and tested with available data for 17 European countries, USA, China, Japan, Russia, and India for the years 2001 to 2008. The ANN modeling was performed using general regression neural network (GRNN), and correlation and variance inflation factor (VIF) analysis were applied to reduce the number of input variables. The best results were obtained using the selection of inputs based on the correlation between input variables, which provided a more accurate prediction than the GRNN model created with all initial selected input variables. Sensitivity analysis showed that the input variables with the largest influences on the GRNN model results were (in descending order) electricity production from oil sources, agricultural land, fossil fuel energy consumption, number of vehicles, gross domestic product, energy use, and electricity production from coal sources.
PB  - Springer, Dordrecht
T2  - Air Quality Atmosphere and Health
T1  - Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model
EP  - 23
IS  - 1
SP  - 15
VL  - 10
DO  - 10.1007/s11869-016-0403-6
ER  - 

@article{
author = "Stamenković, Lidija J. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2017",
abstract = "Nitrogen oxides (NOx) emissions into the atmosphere have multiple negative effects on the environment and effects directly and indirectly on human health. This paper describes the development of a model for NO (x) emission prediction at the national level based on artificial neural networks (ANNs) and on widely available sustainability, industrial, and economical parameters as input variables. In this study, 11 sustainability, industrial, and economical parameters were chosen as potential input variables. The ANN models were trained, validated, and tested with available data for 17 European countries, USA, China, Japan, Russia, and India for the years 2001 to 2008. The ANN modeling was performed using general regression neural network (GRNN), and correlation and variance inflation factor (VIF) analysis were applied to reduce the number of input variables. The best results were obtained using the selection of inputs based on the correlation between input variables, which provided a more accurate prediction than the GRNN model created with all initial selected input variables. Sensitivity analysis showed that the input variables with the largest influences on the GRNN model results were (in descending order) electricity production from oil sources, agricultural land, fossil fuel energy consumption, number of vehicles, gross domestic product, energy use, and electricity production from coal sources.",
publisher = "Springer, Dordrecht",
journal = "Air Quality Atmosphere and Health",
title = "Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model",
pages = "23-15",
number = "1",
volume = "10",
doi = "10.1007/s11869-016-0403-6"
}

Stamenković, L. J., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2017). Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model. in Air Quality Atmosphere and Health
Springer, Dordrecht., 10(1), 15-23.
https://doi.org/10.1007/s11869-016-0403-6

Stamenković LJ, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model. in Air Quality Atmosphere and Health. 2017;10(1):15-23.
doi:10.1007/s11869-016-0403-6 .

Stamenković, Lidija J., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model" in Air Quality Atmosphere and Health, 10, no. 1 (2017):15-23,
https://doi.org/10.1007/s11869-016-0403-6 . .

TY  - JOUR
AU  - Stamenković, Lidija J.
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2016
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3279
AB  - This paper describes the development of an artificial neural network (ANN) model based on economical and sustainability indicators for the prediction of annual non-methane volatile organic compounds (NMVOCs) emissions in China for the period 2005-2011 and its comparison with inventory emission factor models. The NMVOCs emissions in China were estimated using ANN model which was created using available data for nine European countries, which NMVOC emission per capita approximately correspond to the Chinese emissions, for the period 2004-2012. The forward input selection strategy was used to compare the significance of particular inputs for the prediction of NMVOC emissions in the nine selected EU countries and China. The final ANN model was trained using only five input variables, and it has demonstrated similar accuracy in predicting NMVOC emissions for the selected EU countries that were used for the development of the model and then for China for which the input dataset was previously unknown to the ANN model. The obtained mean absolute percentage error (MAPE) values were 8 % for EU countries and 5 % for China. Also, the temporal trend of NMVOC emissions predicted in this study is generally consistent with the trend obtained using inventory emission models. The proposed ANN approach can represent a viable alternative for the prediction of NMVOC emissions at the national level, in particular for developing countries which are usually lacking emission data.
PB  - Springer Heidelberg, Heidelberg
T2  - Environmental Science and Pollution Research
T1  - Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs
EP  - 10762
IS  - 11
SP  - 10753
VL  - 23
DO  - 10.1007/s11356-016-6279-z
ER  - 

@article{
author = "Stamenković, Lidija J. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2016",
abstract = "This paper describes the development of an artificial neural network (ANN) model based on economical and sustainability indicators for the prediction of annual non-methane volatile organic compounds (NMVOCs) emissions in China for the period 2005-2011 and its comparison with inventory emission factor models. The NMVOCs emissions in China were estimated using ANN model which was created using available data for nine European countries, which NMVOC emission per capita approximately correspond to the Chinese emissions, for the period 2004-2012. The forward input selection strategy was used to compare the significance of particular inputs for the prediction of NMVOC emissions in the nine selected EU countries and China. The final ANN model was trained using only five input variables, and it has demonstrated similar accuracy in predicting NMVOC emissions for the selected EU countries that were used for the development of the model and then for China for which the input dataset was previously unknown to the ANN model. The obtained mean absolute percentage error (MAPE) values were 8 % for EU countries and 5 % for China. Also, the temporal trend of NMVOC emissions predicted in this study is generally consistent with the trend obtained using inventory emission models. The proposed ANN approach can represent a viable alternative for the prediction of NMVOC emissions at the national level, in particular for developing countries which are usually lacking emission data.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Environmental Science and Pollution Research",
title = "Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs",
pages = "10762-10753",
number = "11",
volume = "23",
doi = "10.1007/s11356-016-6279-z"
}

Stamenković, L. J., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2016). Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs. in Environmental Science and Pollution Research
Springer Heidelberg, Heidelberg., 23(11), 10753-10762.
https://doi.org/10.1007/s11356-016-6279-z

Stamenković LJ, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs. in Environmental Science and Pollution Research. 2016;23(11):10753-10762.
doi:10.1007/s11356-016-6279-z .

Stamenković, Lidija J., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs" in Environmental Science and Pollution Research, 23, no. 11 (2016):10753-10762,
https://doi.org/10.1007/s11356-016-6279-z . .

TY  - JOUR
AU  - Stamenković, Lidija J.
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2015
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2954
AB  - The aim of this work was to create an ANN model for the prediction of GHG emissions in the Republic of Serbia in the period 1995-2013. Because of the lack of GHG emission data and inputs for Serbia, the ANN model was initially developed for Bulgaria, using the gross domestic product per capita (GDP) and annual energy production per capita (GPE) as inputs. The model was trained and validated with the data for Bulgaria for the period from 1990 to 2011,using Recurrent Neural Network (RNN) architecture. The results obtained for Serbia indicate a good agreement between the calculated and predicted GHG emissions in 1998, with an error of only 3%. The predicted change in the emission of GHG in the studied period is in agreement with social and economical factors that can be related to the changes in GHG emissions(e.g. emission reduction of about 10% in 1999). The RNN model predicts that the GHG emissions in 2013was above the level in 1998, but still lower than the GHG emissions calculated for the year 1990.
AB  - Cilj ovog rada je bio kreiranje modela, zasnovanog na veštačkim neuronskim mrežama, za predviđanje emisije GHG u Republici Srbiji. Zbog nedostatka podataka o GHG emisiji u Republici Srbiji, model je razvijen korišćenjem podataka za Republiku Bugarsku, a zatim je primenjen i na Republiku Srbiju. Kao ulazni parametri korišćeni su bruto d omaći proizvod po stanovniku (BDP) i godišnja proizvodnja energije po stanovniku (GPE). Model je treniran i testiran sa podacima za Bugarsku za period od 1990 do 2011. godine, pri čemu je korišćena rekurentna neuronska mreža (RNN). Rezultati za Republiku Srbiju pokazuju dobro slaganje između proračunate i modelom predviđene vrednosti emisije GHG za 1998. godinu, sa odstupanjem od svega 3%. Analiza predviđenog trenda ukazuje na promene u emisiji koje su bile posledica društvenih okolnosti (smanjenje emisije od oko 10% tokom 1999. godine). Model pokazuje da je emisija GHG-a u 2013. godini prestigla nivo iz 1998. godine, ali će i dalje biti ispod vrednosti emisije proračunate za 1990. godinu.
PB  - Naučno-stručno društvo za zaštitu životne sredine Srbije - Ecologica, Beograd
T2  - Ecologica
T1  - Estimation of GHG emission in Serbia for period 1995-2013 using recurent neural networks
T1  - Emisije gasova staklene bašte u Srbiji za period 1995-2013. godina primenom rekurentnih neuronskih mreža
EP  - 492
IS  - 79
SP  - 488
VL  - 22
UR  - https://hdl.handle.net/21.15107/rcub_technorep_2954
ER  - 

@article{
author = "Stamenković, Lidija J. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2015",
abstract = "The aim of this work was to create an ANN model for the prediction of GHG emissions in the Republic of Serbia in the period 1995-2013. Because of the lack of GHG emission data and inputs for Serbia, the ANN model was initially developed for Bulgaria, using the gross domestic product per capita (GDP) and annual energy production per capita (GPE) as inputs. The model was trained and validated with the data for Bulgaria for the period from 1990 to 2011,using Recurrent Neural Network (RNN) architecture. The results obtained for Serbia indicate a good agreement between the calculated and predicted GHG emissions in 1998, with an error of only 3%. The predicted change in the emission of GHG in the studied period is in agreement with social and economical factors that can be related to the changes in GHG emissions(e.g. emission reduction of about 10% in 1999). The RNN model predicts that the GHG emissions in 2013was above the level in 1998, but still lower than the GHG emissions calculated for the year 1990., Cilj ovog rada je bio kreiranje modela, zasnovanog na veštačkim neuronskim mrežama, za predviđanje emisije GHG u Republici Srbiji. Zbog nedostatka podataka o GHG emisiji u Republici Srbiji, model je razvijen korišćenjem podataka za Republiku Bugarsku, a zatim je primenjen i na Republiku Srbiju. Kao ulazni parametri korišćeni su bruto d omaći proizvod po stanovniku (BDP) i godišnja proizvodnja energije po stanovniku (GPE). Model je treniran i testiran sa podacima za Bugarsku za period od 1990 do 2011. godine, pri čemu je korišćena rekurentna neuronska mreža (RNN). Rezultati za Republiku Srbiju pokazuju dobro slaganje između proračunate i modelom predviđene vrednosti emisije GHG za 1998. godinu, sa odstupanjem od svega 3%. Analiza predviđenog trenda ukazuje na promene u emisiji koje su bile posledica društvenih okolnosti (smanjenje emisije od oko 10% tokom 1999. godine). Model pokazuje da je emisija GHG-a u 2013. godini prestigla nivo iz 1998. godine, ali će i dalje biti ispod vrednosti emisije proračunate za 1990. godinu.",
publisher = "Naučno-stručno društvo za zaštitu životne sredine Srbije - Ecologica, Beograd",
journal = "Ecologica",
title = "Estimation of GHG emission in Serbia for period 1995-2013 using recurent neural networks, Emisije gasova staklene bašte u Srbiji za period 1995-2013. godina primenom rekurentnih neuronskih mreža",
pages = "492-488",
number = "79",
volume = "22",
url = "https://hdl.handle.net/21.15107/rcub_technorep_2954"
}

Stamenković, L. J., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2015). Estimation of GHG emission in Serbia for period 1995-2013 using recurent neural networks. in Ecologica
Naučno-stručno društvo za zaštitu životne sredine Srbije - Ecologica, Beograd., 22(79), 488-492.
https://hdl.handle.net/21.15107/rcub_technorep_2954

Stamenković LJ, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Estimation of GHG emission in Serbia for period 1995-2013 using recurent neural networks. in Ecologica. 2015;22(79):488-492.
https://hdl.handle.net/21.15107/rcub_technorep_2954 .

Stamenković, Lidija J., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Estimation of GHG emission in Serbia for period 1995-2013 using recurent neural networks" in Ecologica, 22, no. 79 (2015):488-492,
https://hdl.handle.net/21.15107/rcub_technorep_2954 .

TY  - JOUR
AU  - Stamenković, Lidija J.
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2015
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2997
AB  - The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using artificial neural networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a backpropagation neural network (BPNN) and a general regression neural network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare the model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model could be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique, which could be used to support the implementation of sustainable development strategies and environmental management policies.
PB  - Srpsko hemijsko društvo, Beograd
T2  - Journal of the Serbian Chemical Society
T1  - Modeling of methane emissions using the artificial neural network approach
EP  - 433
IS  - 3
SP  - 421
VL  - 80
DO  - 10.2298/JSC020414110S
ER  - 

@article{
author = "Stamenković, Lidija J. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
year = "2015",
abstract = "The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using artificial neural networks (ANN) with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a backpropagation neural network (BPNN) and a general regression neural network (GRNN). A conventional multiple linear regression (MLR) model was also developed in order to compare the model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model could be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique, which could be used to support the implementation of sustainable development strategies and environmental management policies.",
publisher = "Srpsko hemijsko društvo, Beograd",
journal = "Journal of the Serbian Chemical Society",
title = "Modeling of methane emissions using the artificial neural network approach",
pages = "433-421",
number = "3",
volume = "80",
doi = "10.2298/JSC020414110S"
}

Stamenković, L. J., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2015). Modeling of methane emissions using the artificial neural network approach. in Journal of the Serbian Chemical Society
Srpsko hemijsko društvo, Beograd., 80(3), 421-433.
https://doi.org/10.2298/JSC020414110S

Stamenković LJ, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Modeling of methane emissions using the artificial neural network approach. in Journal of the Serbian Chemical Society. 2015;80(3):421-433.
doi:10.2298/JSC020414110S .

Stamenković, Lidija J., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Modeling of methane emissions using the artificial neural network approach" in Journal of the Serbian Chemical Society, 80, no. 3 (2015):421-433,
https://doi.org/10.2298/JSC020414110S . .

TY  - JOUR
AU  - Stamenković, Lidija J.
AU  - Antanasijević, Davor
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
AU  - Pocajt, Viktor
PY  - 2015
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3099
AB  - 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 %.
PB  - Springer Heidelberg, Heidelberg
T2  - Environmental Science and Pollution Research
T1  - Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach
EP  - 18858
IS  - 23
SP  - 18849
VL  - 22
DO  - 10.1007/s11356-015-5075-5
ER  - 

@article{
author = "Stamenković, Lidija J. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor",
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 %.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Environmental Science and Pollution Research",
title = "Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach",
pages = "18858-18849",
number = "23",
volume = "22",
doi = "10.1007/s11356-015-5075-5"
}

Stamenković, L. J., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, V.. (2015). Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach. in Environmental Science and Pollution Research
Springer Heidelberg, Heidelberg., 22(23), 18849-18858.
https://doi.org/10.1007/s11356-015-5075-5

Stamenković LJ, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V. Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach. in Environmental Science and Pollution Research. 2015;22(23):18849-18858.
doi:10.1007/s11356-015-5075-5 .

Stamenković, Lidija J., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach" in Environmental Science and Pollution Research, 22, no. 23 (2015):18849-18858,
https://doi.org/10.1007/s11356-015-5075-5 . .