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 cap...abilities 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.
Keywords:
Medical waste / Healthcare waste / Hazardous waste / Chemical waste / Artificial neural networks
Source:
Journal of Material Cycles and Waste Management, 2018, 20, 3, 1736-1750
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 .
