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Journal of Material Cycles and Waste Management

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30.04.2018 | ORIGINAL ARTICLE

An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level

verfasst von: Vladimir M. Adamović, Davor Z. Antanasijević, Mirjana Đ. Ristić, Aleksandra A. Perić-Grujić, Viktor V. Pocajt

Erschienen in: Journal of Material Cycles and Waste Management | Ausgabe 3/2018

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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.

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European Parliament and the Council of the European Union (2008) Directive 2008/98/EC of the European Parliament and of the Council on waste and repealing certain directives (Waste framework). OJ L 312,22.11.2008, 3–30. doi:2008/98/EC.; 32008L0098

Kang Y-Y, Jeon T-W, Kim W-I, Shin SK, Yeon J-M, Somasundaram S (2014) Correlation study of hazardous waste characteristics among various chemical processes in Republic of Korea. J Mater Cycles Waste Manag 16(3):566–575. https://doi.org/10.1007/s10163-013-0208-8 CrossRef

Court CD, Munday M, Roberts A, Turner K (2015) Can hazardous waste supply chain “hotspots” be identified using an input-output framework? Eur J of Oper Res 241(1):177–187. https://doi.org/10.1016/j.ejor.2014.08.011 CrossRef

Schuhmacher M, Fàbrega F, Kumar V, García F, Nadal M, Domingo JL (2014) A PBPK model to estimate PCDD/F levels in adipose tissue: Comparison with experimental values of residents near a hazardous waste incinerator. Environ Int 73:150–157. https://doi.org/10.1016/j.envint.2014.07.020 CrossRef

Zakaria B, Abdullah R, Ramli MF, Latif PA (2012) Selection criteria using the Delphi method for siting an integrated hazardous waste disposal facility in Malaysia. J Environ Plann Manag 56(4):1–19. https://doi.org/10.1080/09640568.2012.689614

United Nations (2007) Indicators of Sustainable Development: Guidelines and Methodologies. Third Edit. United Nations publication, New York, USA. http://www.un.org/esa/sustdev/natlinfo/indicators/guidelines.pdf

van Beusekom C (1999) Hazardous waste in the European Union. Statistics in focus—Environment and energy, Luxembourg: Eurostat 1–7. http://edz.bib.uni-mannheim.de/www-edz/pdf/statinf/99/CA-NQ-99-007-EN-I-EN.pdf. Accessed 22 Mar 2016

European Commission Eurostat (2017) Waste statistics—statistics explained. http://ec.europa.eu/eurostat/statistics-explained/index.php/Waste_statistics. Accessed 22 Mar 2016

EPA US (2012). Chemical safety for sustainability chemical, strategic research action plan 2012–2016. EPA 601/R-12/006:pp 1–64. http://www.epa.gov/sites/production/files/2014-06/documents/css-strap.pdf. Accessed 4 May 2016

10.

Rudel RA, Attfield KR, Schifano JN, Brody JG (2007) Chemicals causing mammary gland tumors in animals signal new directions for epidemiology, chemicals testing, and risk assessment for breast cancer prevention. Cancer 109(12):2635–2666. https://doi.org/10.1002/cncr.22653 CrossRef

11.

Komilis D, Fouki A, Papadopoulos D (2012) Hazardous medical waste generation rates of different categories of health-care facilities. Waste Manag 32(7):1434–1441. https://doi.org/10.1016/j.wasman.2012.02.015 CrossRef

12.

Birpinar ME, Bilgili MS, Erdoğan T (2009) Medical waste management in Turkey: a case study of Istanbul. Waste Manag 29(1):445–448. https://doi.org/10.1016/j.wasman.2008.03.015 CrossRef

13.

Mmereki D, Baldwin A, Li B, Liu M (2017) Healthcare waste management in Botswana: storage, collection, treatment and disposal system. J Mater Cycles Waste Manag 19(1):351–365. https://doi.org/10.1007/s10163-015-0429-0 CrossRef

14.

Rushbrook P, Zghondi R (2005) Better healthcare waste management: an integral component of health investment. World Health Organization, Amman http://apps.who.int/iris/bitstream/10665/119762/1/dsa515.pdf. Accessed 22. Mar 2016

15.

Shannon AL, Woolridge A (2011) Chap. 23 - Medical Waste. Elsevier Inc. https://doi.org/10.1016/B978-0-12-381475-3.10023-3

16.

Sartaj M, Arabgol R (2015) Assessment of healthcare waste management practices and associated problems in Isfahan Province (Iran). J Mater Cycles Waste Manag 17(1):99–106. https://doi.org/10.1007/s10163-014-0230-5 CrossRef

17.

Elimelech E, Ayalon O, Flicstein B (2011) Hazardous waste management and weight-based indicators - The case of Haifa Metropolis. J Hazard Mater 185(2–3):626–633. https://doi.org/10.1016/j.jhazmat.2010.09.064 CrossRef

18.

Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576. https://doi.org/10.1109/72.97934 CrossRef

19.

Antanasijević D, Pocajt V, Popović I, Redžić N, Ristić M (2013) The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustain Sci 8(1):37–46. https://doi.org/10.1007/s11625-012-0161-9 CrossRef

20.

Adamović V, Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V (2017) Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis. Environ Sci Pollut Res 24(1):299–311. https://doi.org/10.1007/s11356-016-7767-x CrossRef

21.

Statistics Norway (2016) Hazardous waste, 2015. Oslo. http://www.ssb.no/en/natur-og-miljo/statistikker/spesavf/aar. Accessed 18 May 2017

22.

Gusca J, Kalnins SN, Blumberga D, Bozhko L, Khabdullina Z, Khabdullin A (2015) Assessment method of health care waste generation in Latvia and Kazakhstan. Energy Proc 72:175–179. https://doi.org/10.1016/j.egypro.2015.06.025 CrossRef

23.

Ben-Gal I (2005) Outlier Detection. In: Maimon O, Rockach L (eds). Data mining and knowledge discovery handbook: a complete guide for practitioners and researchers. Kluwer Academic Publishers pp 131–146. https://doi.org/10.1007/0-387-25465-x_7

24.

Liu H, Shah S, Jiang W (2004) On-line outlier detection and data cleaning. Comput Chem Eng 28(9):1635–1647. https://doi.org/10.1016/j.compchemeng.2004.01.009 CrossRef

25.

Beigl P, Lebersorger S, Salhofer S (2008) Modelling municipal solid waste generation: a review. Waste Manag 28(1):200–214. https://doi.org/10.1016/j.wasman.2006.12.011 CrossRef

26.

Sabour MR, Mohamedifard A, Kamalan H (2007) A mathematical model to predict the composition and generation of hospital wastes in Iran. Waste Manag 27(4):584–587. https://doi.org/10.1016/j.wasman.2006.05.010 CrossRef

27.

Jahandideh S, Jahandideh S, Asadabadi EB, Askarian M, Movahedi MM, Hosseini S et al (2009) The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. Waste Manag 29(11):2874–2879. https://doi.org/10.1016/j.wasman.2009.06.027 CrossRef

28.

Eleyan D, Al-Khatib I, Garfield J (2013) System dynamics model for hospital waste characterization and generation in developing countries. Waste Manag Res 31(10):986–995. https://doi.org/10.1177/0734242X13490981 CrossRef

29.

Al-Khatib IA, Abu Fkhidah I, Khatib JI, Kontogianni S (2016) Implementation of a multi-variable regression analysis in the assessment of the generation rate and composition of hospital solid waste for the design of a sustainable management system in developing countries. Waste Manag Res 34(3):225–234. https://doi.org/10.1177/0734242X15622813 CrossRef

30.

Karpušenkaitė A, Ruzgas T, Denafas G (2016) Forecasting medical waste generation using short and extra short datasets: case study of Lithuania. Waste Manag Res 34(4):378–387. https://doi.org/10.1177/0734242X16628977 CrossRef

31.

Granados AJ, Peterson PJ (1999) Hazardous waste indicators for national decision makers. J Environ Manage 55(4):249–263. https://doi.org/10.1006/jema.1999.0254 CrossRef

32.

Tripathy M (2010) Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network. Simul Model Pract Theory 18(5):600–611. https://doi.org/10.1016/j.simpat.2010.01.003 CrossRef

33.

Mustapha A, Aris AZ, Juahir H, Ramli MF, Kura NU (2013) River water quality assessment using environmentric techniques: case study of Jakara River Basin. Environ Sci Pollut Res 20(8):5630–5644. https://doi.org/10.1007/s11356-013-1542-z CrossRef

34.

Hamilton LC (1991) Modern data analysis: a first course in applied statistics. Technometrics 33(4):487–488

35.

Walczak S, Cerpa N (1999) Heuristic principles for the design of artificial neural networks. Inf Softw Technol 41(2):107–117. https://doi.org/10.1016/S0950-5849(98)00116-5 CrossRef

36.

Gheyas IA, Smith LS (2010) A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 73(16–18):3039–3065. https://doi.org/10.1016/j.neucom.2010.06.021 CrossRef

37.

Tomandl D, Schober A (2001) A modified general regression neural network (MGRNN) with new, efficient training algorithms as a robust “black box”-tool for data analysis. Neural Netw 14(8):1023–1034. https://doi.org/10.1016/S0893-6080(01)00051-X CrossRef

38.

Zhou Q, Jiang H, Wang J, Zhou J (2014) A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci Total Environ 496(1):264–274. https://doi.org/10.1016/j.scitotenv.2014.07.051 CrossRef

39.

Gheyas IA, Smith LS (2011) A novel neural network ensemble architecture for time series forecasting. Neurocomputing 74(18):3855–3864. https://doi.org/10.1016/j.neucom.2011.08.005 CrossRef

40.

Millie DF, Weckman GR, Young W, Ivey JE, Carrick HJ, Fahnenstiel GL (2012) Modeling microalgal abundance with artificial neural networks: demonstration of a heuristic “Grey-Box” to deconvolve and quantify environmental influences. Environ Model Softw 38(1):27–39. https://doi.org/10.1016/j.envsoft.2012.04.009 CrossRef

41.

Kisi O (2006) Generalized regression neural networks for evapotranspiration modelling. J Hydrol Sci 51(6):1092–1105. https://doi.org/10.1623/hysj.51.6.1092 CrossRef

42.

Palani S, Liong SY, Tkalich P (2008) An ANN application for water quality forecasting. Mar Poll Bull 56(9):1586–1597. https://doi.org/10.1016/j.marpolbul.2008.05.021 CrossRef

43.

Šiljić A, Antanasijević D, Perić-Grujić A, Ristić M, Pocajt V (2014) Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations. Environ Sci Pollut Res 22(6):4230–4241. https://doi.org/10.1007/s11356-014-3669-y

44.

Yaseen ZM, El-shafie A, Jaafar O, Afan HA, Sayl KN (2015) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844. https://doi.org/10.1016/j.jhydrol.2015.10.038 CrossRef

45.

Specht DF (1990) Probabilistic neural networks. Neural Netw 3(1):109–118. https://doi.org/10.1016/0893-6080(90)90049-Q CrossRef

46.

Sawant SS, Topannavar PS (2015) Introduction to probabilistic neural network—used for image classifications. Int J Adv Res Comput Sci Softw Eng 5(4):279–283

47.

Antanasijević D, Ristić M, Perić-Grujić A, Pocajt V (2014) Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. Int J Greenh Gas Control 20:244–253. https://doi.org/10.1016/j.ijggc.2013.11.011 CrossRef

48.

Sözen A, Gülseven Z, Arcaklioğlu E (2007) Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies. Energy Policy 35(12):6491–6505. https://doi.org/10.1016/j.enpol.2007.08.024 CrossRef

49.

Pahlavan R, Omid M, Akram A (2012) Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy 37(1):171–176. https://doi.org/10.1016/j.energy.2011.11.055 CrossRef

50.

Kialashaki A (2014) Evaluation and Forecast of Energy Consumption in Different Sectors of the United States Using Artificial Neural Networks. Theses and Dissertations. Paper 628. University of Wisconsin-Milwaukee

51.

Jacobs G, Šlaus I (2010) Indicators of economic progress: the power of measurement and human welfare. CADMUS J 1(1):53–113

52.

Costanza R, Hart M, Posner S, Talberth J (2009) Beyond GDP: the need for new measures of progress.The Pardee papers no. 4 - January 2009. Boston University, Boston, MA, USA. https://www.bu.edu/pardee/files/documents/PP-004-GDP.pdf

53.

Dogan E, Sengorur B, Koklu R (2009) Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique. J Environ Manage 90(2):1229–1235. https://doi.org/10.1016/j.jenvman.2008.06.004 CrossRef

54.

CIWM (2014) An introductory guide to healthcare waste management in England and Wales. Northampton, UK. https://www.ciwm-journal.co.uk/downloads/Healthcare-Waste-WEB.pdf. Accessed 8 Apr 2018

55.

Insa E, Zamorano M, López R (2010) Critical review of medical waste legislation in Spain. Resour Conserv Recycl 54(12):1048–1059. https://doi.org/10.1016/j.resconrec.2010.06.005 CrossRef

56.

Noori R, Karbassi A, Salman Sabahi M (2010) Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. J Environ Manage 91:767–771. https://doi.org/10.1016/j.jenvman.2009.10.007 CrossRef

57.

Rimaityte I, Ruzgas T, Denafas G, Racys V, Martuzevicius D (2012) Application and evaluation of forecasting methods for municipal solid waste generation in an eastern-European city. Waste Manag Res 30:89–98. https://doi.org/10.1177/0734242X10396754 CrossRef

58.

Eurostat (2016) Glossary: purchasing power standard (PPS)—statistics explained. http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Purchasing_power_standard. Accessed 20 Apr 2016

59.

NSI (2016) GDP at Market prices per inhabitant in Euro and in PPS | National statistical institute Republic of Bulgaria. http://www.nsi.bg/en/content/5226/gdp-market-prices-inhabitant-euro-and-pps. Accessed 20 Apr 2016

60.

Weisz H, Krausmann F, Amann C, Eisenmenger N, Erb KH, Hubacek K, Fisher-Kowalski (2006) The physical economy of the European Union: cross-country comparison and determinants of material consumption. Ecol Econ 58(4):676–698. https://doi.org/10.1016/j.ecolecon.2005.08.016 CrossRef

61.

Harttgen K, Klasen S (2012) A household-based human development index. World Dev 40(5):878–899. https://doi.org/10.1016/j.worlddev.2011.09.011 CrossRef

62.

UNDP (2015) Human development report 2015. Work for human development. New York http://hdr.undp.org/sites/default/files/2015_human_development_report.pdf. Accessed 5 May 2016

63.

The World Bank (2014) World Development Indicators 2014. Washington: International Bank for Reconstruction and Development/The World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/18237/9781464801631.pdf?sequence=1&isAllowed=y. Accessed 5 May 2016

64.

Saygin D, Patel M, Tam C, Gielen D (2009) Chemical and Petrochemical sector. Potential of best practice technology and other measures for improving energy efficiency. IEA Information Paper OECD/IEA, pp 1–60

65.

Eurostat (2016) Simplified energy balances - annual data http://ec.europa.eu/eurostat/web/products-datasets/-/nrg_100a. Accessed 5 May 2016

66.

OECD (2002) Proposed standard practice for surveys of research and experimental development: “Frascati Manual 2002”, the measurement of scientific and technological activities. OECD Publications, Paris

67.

The World Bank (2016) Life expectancy at birth. http://data.worldbank.org/indicator/SP.DYN.LE00.IN. Accessed 6 May 2016

68.

Ward T (2008) Material deprivation in the EU (Chapter 5). In: Ward T, Lelkes O, Sutherland H, Tóth I G (eds) Social inclusion and income distribution in the European Union - 2008, European Commission, Tárki, Budapest. pp 114–128. http://ec.europa.eu/social/BlobServlet?docId=3991&langId=en

69.

Eurostat (2016) Inability to face unexpected financial expenses (source: SILC) - Eurostat. http://ec.europa.eu/eurostat/en/web/products-datasets/-/ILC_MDES04. Accessed 6 May 2016

70.

Eurostat (2016) SCL—International statistical classification of diseases and related health problems (ICD-10 2007). RAMON-Reference and Management of Nomenclatures. http://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=LST_NOM_DTL_LINEAR&IntCurrentPage=1&StrNom=CL_ICD10&StrLanguageCode=EN. Accessed 17 Oct 2016

71.

Renaud O, Victoria-Feser MP (2010) A robust coefficient of determination for regression. J Stat Plan Inference 140(7):1852–1862. https://doi.org/10.1016/j.jspi.2010.01.008 MathSciNetCrossRefMATH

72.

Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250. https://doi.org/10.5194/gmd-7-1247-2014 CrossRef

73.

Pao H-T, Fu H-C, Tseng C-L (2012) Forecasting of CO₂ emissions, energy consumption and economic growth in China using an improved grey model. Energy 40(1):400–409. https://doi.org/10.1016/j.energy.2012.01.037 CrossRef

74.

Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32(13):2088–2094. https://doi.org/10.1002/joc.2419 CrossRef

Titel: An optimized artificial neural network model for the prediction of rate of hazardous chemical and healthcare waste generation at the national level
verfasst von: Vladimir M. Adamović
Davor Z. Antanasijević
Mirjana Đ. Ristić
Aleksandra A. Perić-Grujić
Viktor V. Pocajt
Publikationsdatum: 30.04.2018
Verlag: Springer Japan
Erschienen in: Journal of Material Cycles and Waste Management / Ausgabe 3/2018
Print ISSN: 1438-4957
Elektronische ISSN: 1611-8227
DOI: https://doi.org/10.1007/s10163-018-0741-6

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