Estimation of NMVOC emissions using artificial neural networks and economical and sustainability indicators as inputs
Samo za registrovane korisnike
2016
Autori
Stamenković, Lidija J.Antanasijević, Davor
Ristić, Mirjana
Perić-Grujić, Aleksandra
Pocajt, Viktor
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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 previousl...y 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.
Ključne reči:
ANN / China / Emissions / NMVOC / ModelingIzvor:
Environmental Science and Pollution Research, 2016, 23, 11, 10753-10762Izdavač:
- Springer Heidelberg, Heidelberg
Finansiranje / projekti:
- Razvoj i primena metoda i materijala za monitoring novih zagađujućih i toksičnih organskih materija i teških metala (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1007/s11356-016-6279-z
ISSN: 0944-1344
PubMed: 26888640
WoS: 000377021500040
Scopus: 2-s2.0-84958767328
Institucija/grupa
Tehnološko-metalurški fakultetTY - 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 . .