An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level
Само за регистроване кориснике
2018
Аутори
Adamović, Vladimir M.Antanasijević, Davor
Ristić, Mirjana
Perić-Grujić, Aleksandra
Pocajt, Viktor
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Hazardous waste / Chemical waste / Healthcare waste / Medical waste / Artificial neural networksИзвор:
Journal of Material Cycles and Waste Management, 2018, 20, 3, 1736-1750Издавач:
- Springer, New York
Финансирање / пројекти:
- Развој и примена метода и материјала за мониторинг нових загађујућих и токсичних органских материја и тешких метала (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1007/s10163-018-0741-6
ISSN: 1438-4957
WoS: 000435811400033
Scopus: 2-s2.0-85048873652
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Adamović, Vladimir M. AU - Antanasijević, Davor AU - Ristić, Mirjana AU - Perić-Grujić, Aleksandra AU - Pocajt, Viktor PY - 2018 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3994 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 ER -
@article{ author = "Adamović, Vladimir M. and Antanasijević, Davor and Ristić, Mirjana and Perić-Grujić, Aleksandra and Pocajt, Viktor", 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" }
Adamović, V. M., Antanasijević, D., Ristić, M., Perić-Grujić, A.,& Pocajt, 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
Adamović VM, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt 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. 2018;20(3):1736-1750. doi:10.1007/s10163-018-0741-6 .
Adamović, Vladimir M., Antanasijević, Davor, Ristić, Mirjana, Perić-Grujić, Aleksandra, Pocajt, Viktor, "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 . .