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PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization

Authorized Users Only
2013
Authors
Antanasijević, Davor
Pocajt, Viktor
Povrenović, Dragan
Ristić, Mirjana
Perić-Grujić, Aleksandra
Article (Published version)
Metadata
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Abstract
This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM10 emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM10 emission data, collected through the Convention ...on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM10 emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM10 emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.

Keywords:
Neural networks / Multiple linear regression / Principal component regression / Annual PM10 emission forecasting
Source:
Science of the Total Environment, 2013, 443, 511-519
Publisher:
  • Elsevier Science Bv, Amsterdam
Funding / projects:
  • Development and Application of Methods and Materials for Monitoring New Organic Contaminants, Toxic Compounds and Heavy Metals (RS-172007)

DOI: 10.1016/j.scitotenv.2012.10.110

ISSN: 0048-9697

PubMed: 23220141

WoS: 000315559900055

Scopus: 2-s2.0-84870298794
[ Google Scholar ]
132
111
URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2539
Collections
  • Radovi istraživača / Researchers’ publications (TMF)
Institution/Community
Tehnološko-metalurški fakultet
TY  - JOUR
AU  - Antanasijević, Davor
AU  - Pocajt, Viktor
AU  - Povrenović, Dragan
AU  - Ristić, Mirjana
AU  - Perić-Grujić, Aleksandra
PY  - 2013
UR  - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/2539
AB  - This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM10 emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM10 emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM10 emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM10 emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.
PB  - Elsevier Science Bv, Amsterdam
T2  - Science of the Total Environment
T1  - PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization
EP  - 519
SP  - 511
VL  - 443
DO  - 10.1016/j.scitotenv.2012.10.110
ER  - 
@article{
author = "Antanasijević, Davor and Pocajt, Viktor and Povrenović, Dragan and Ristić, Mirjana and Perić-Grujić, Aleksandra",
year = "2013",
abstract = "This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM10 emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM10 emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM10 emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM10 emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.",
publisher = "Elsevier Science Bv, Amsterdam",
journal = "Science of the Total Environment",
title = "PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization",
pages = "519-511",
volume = "443",
doi = "10.1016/j.scitotenv.2012.10.110"
}
Antanasijević, D., Pocajt, V., Povrenović, D., Ristić, M.,& Perić-Grujić, A.. (2013). PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. in Science of the Total Environment
Elsevier Science Bv, Amsterdam., 443, 511-519.
https://doi.org/10.1016/j.scitotenv.2012.10.110
Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. in Science of the Total Environment. 2013;443:511-519.
doi:10.1016/j.scitotenv.2012.10.110 .
Antanasijević, Davor, Pocajt, Viktor, Povrenović, Dragan, Ristić, Mirjana, Perić-Grujić, Aleksandra, "PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization" in Science of the Total Environment, 443 (2013):511-519,
https://doi.org/10.1016/j.scitotenv.2012.10.110 . .

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