TY  - JOUR
AU  - Adamović, Vladimir
AU  - Antanasijević, Davor Z.
AU  - Ristić, Mirjana D.
AU  - Perić-Grujić, Aleksandra A.
AU  - Pocajt, Viktor V.
PY  - 2018
UR  - https://ritnms.itnms.ac.rs/handle/123456789/482
AB  - This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level.
PB  - Springer, New York
T2  - Journal of Material Cycles and Waste Management
T1  - An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level
EP  - 1750
IS  - 3
SP  - 1736
VL  - 20
DO  - 10.1007/s10163-018-0741-6
UR  - conv_825
ER  - 

@article{
author = "Adamović, Vladimir and Antanasijević, Davor Z. and Ristić, Mirjana D. and Perić-Grujić, Aleksandra A. and Pocajt, Viktor V.",
year = "2018",
abstract = "This paper presents a development of general regression neural network (a form of artificial neural network) models for the prediction of annual quantities of hazardous chemical and healthcare waste at the national level. Hazardous waste is being generated from many different sources and therefore it is not possible to conduct accurate predictions of the total amount of hazardous waste using traditional methodologies. Since they represent about 40% of the total hazardous waste in the European Union, chemical and healthcare waste were specifically selected for this research. Broadly available social, economic, industrial and sustainability indicators were used as input variables and the optimal sets were selected using correlation analysis and sensitivity analysis. The obtained values of coefficients of determination for the final models were 0.999 for the prediction of chemical hazardous waste and 0.975 for the prediction of healthcare and biological hazardous waste. The predicting capabilities of the models for both types of waste are high, since there were no predictions with errors greater than 25%. Also, results of this research demonstrate that the human development index can replace gross domestic product and in this context even represent a better indicator of socio-economic conditions at the national level.",
publisher = "Springer, New York",
journal = "Journal of Material Cycles and Waste Management",
title = "An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level",
pages = "1750-1736",
number = "3",
volume = "20",
doi = "10.1007/s10163-018-0741-6",
url = "conv_825"
}

Adamović, V., Antanasijević, D. Z., Ristić, M. D., Perić-Grujić, A. A.,& Pocajt, V. V.. (2018). An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. in Journal of Material Cycles and Waste Management
Springer, New York., 20(3), 1736-1750.
https://doi.org/10.1007/s10163-018-0741-6
conv_825

Adamović V, Antanasijević DZ, Ristić MD, Perić-Grujić AA, Pocajt VV. An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level. in Journal of Material Cycles and Waste Management. 2018;20(3):1736-1750.
doi:10.1007/s10163-018-0741-6
conv_825 .

Adamović, Vladimir, Antanasijević, Davor Z., Ristić, Mirjana D., Perić-Grujić, Aleksandra A., Pocajt, Viktor V., "An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level" in Journal of Material Cycles and Waste Management, 20, no. 3 (2018):1736-1750,
https://doi.org/10.1007/s10163-018-0741-6 .,
conv_825 .

TY  - JOUR
AU  - Adamović, Vladimir
AU  - Antanasijević, Davor Z.
AU  - Ristić, Mirjana A.
AU  - Perić-Grujić, Aleksandra A.
AU  - Pocajt, Viktor V.
PY  - 2017
UR  - https://ritnms.itnms.ac.rs/handle/123456789/423
AB  - This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.
PB  - Springer Heidelberg, Heidelberg
T2  - Environmental science and pollution research
T1  - Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis
EP  - 311
IS  - 1
SP  - 299
VL  - 24
DO  - 10.1007/s11356-016-7767-x
UR  - conv_783
ER  - 

@article{
author = "Adamović, Vladimir and Antanasijević, Davor Z. and Ristić, Mirjana A. and Perić-Grujić, Aleksandra A. and Pocajt, Viktor V.",
year = "2017",
abstract = "This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.",
publisher = "Springer Heidelberg, Heidelberg",
journal = "Environmental science and pollution research",
title = "Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis",
pages = "311-299",
number = "1",
volume = "24",
doi = "10.1007/s11356-016-7767-x",
url = "conv_783"
}

Adamović, V., Antanasijević, D. Z., Ristić, M. A., Perić-Grujić, A. A.,& Pocajt, V. V.. (2017). Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis. in Environmental science and pollution research
Springer Heidelberg, Heidelberg., 24(1), 299-311.
https://doi.org/10.1007/s11356-016-7767-x
conv_783

Adamović V, Antanasijević DZ, Ristić MA, Perić-Grujić AA, Pocajt VV. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis. in Environmental science and pollution research. 2017;24(1):299-311.
doi:10.1007/s11356-016-7767-x
conv_783 .

Adamović, Vladimir, Antanasijević, Davor Z., Ristić, Mirjana A., Perić-Grujić, Aleksandra A., Pocajt, Viktor V., "Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis" in Environmental science and pollution research, 24, no. 1 (2017):299-311,
https://doi.org/10.1007/s11356-016-7767-x .,
conv_783 .