Modeling of methane emissions using the artificial neural network approach
Authors
Stamenković, Lidija J.Antanasijević, Davor
Ristić, Mirjana
Perić-Grujić, Aleksandra
Pocajt, Viktor
Article (Published version)
Metadata
Show full item recordAbstract
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.
Keywords:
national emission / general regression neural network / backpropagation neural network / multiple linear regressionSource:
Journal of the Serbian Chemical Society, 2015, 80, 3, 421-433Publisher:
- Srpsko hemijsko društvo, Beograd
Funding / projects:
- Development and Application of Methods and Materials for Monitoring New Organic Contaminants, Toxic Compounds and Heavy Metals (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.2298/JSC020414110S
ISSN: 0352-5139
WoS: 000353423600011
Scopus: 2-s2.0-84930654706
Institution/Community
Tehnološko-metalurški fakultetTY - 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 . .