Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model
Authorized Users Only
2017
Authors
Stamenković, Lidija J.
Antanasijević, Davor

Ristić, Mirjana

Perić-Grujić, Aleksandra

Pocajt, Viktor

Article (Published version)

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Show full item recordAbstract
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.
Keywords:
NOx / Emission prediction / ANN / Correlation analysis / Variance inflation factorSource:
Air Quality Atmosphere and Health, 2017, 10, 1, 15-23Publisher:
- Springer, Dordrecht
Funding / projects:
DOI: 10.1007/s11869-016-0403-6
ISSN: 1873-9318
WoS: 000392140700002
Scopus: 2-s2.0-84963739228
Institution/Community
Tehnološko-metalurški fakultetTY - 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 . .