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Prediction Performance of Separate Collection of Packaging Waste Yields Using Genetic Algorithm Optimized Support Vector Machines

  • V. Sousa
  • I. Meireles
  • V. OliveiraEmail author
  • C. Dias-Ferreira
Original Paper
  • 27 Downloads

Abstract

Understanding the drivers underlying waste production in general, and source-segregated waste in particular, is of utmost importance for waste managers. This work aims at evaluating the performance of support vector machines (SVM) models in the prediction of separate collection yields for packaging waste at municipal level. Two SVM models were developed for a case study of 42 municipalities simultaneously serviced by separate collection of packaging waste and by unsorted waste collection. The “SVM-fixed” model used a fixed set of 5 variables to predict collection yields, whereas the “SVM-optimal” model chose from a pool of 14 variables those that optimized performance, using a genetic algorithm. These SVM models were compared with 3 traditional regression models: the ordinary least square linear (OLS-L), the ordinary least square non-linear (OLS-NL) and robust regression. The robust regression model was further compared against the other regression models in order to assess the influence of the dataset outliers on the model performance. The coefficient of determination, R2, was used to evaluate the performance of these models. The highest performance was attained by the SVM-optimal model (R2 = 0.918), compared to the SVM-fixed model (R2 = 0.670). The performance of the SVM-optimal model was 42% higher than the best performing regression model, the OLS-NL model (R2 = 0.646). The differences in performance among the 3 regression models are small (circa 3%), whereas the exclusion of outliers improved their performance by 13%, indicating that outliers impacted more on performance than the type of traditional regression technique used. The results demonstrate that SVM model can be a viable alternative for prediction of separate collection of packaging waste yields and that there are nine important drivers that all together explain roughly 92% (R2 = 0.918) of the variability in the separate collection yields data.

Keywords

Household packaging waste Regression Separate collection SVM Predictive model 

Notes

Acknowledgements

This work has been funded by Portuguese National Funds through FCT – Portuguese Foundation for Science and Technology under CERNAS (UID/AMB/00681/2013). C Dias-Ferreira and V Oliveira have been funded through FCT “Fundação para a Ciência e para a Tecnologia” by POCH – Programa Operacional Capital Humano within ESF – European Social Fund and by national funds from MCTES (SFRH/BPD/100717/2014; SFRH/BD/115312/2016). V Sousa acknowledges the support from FCT (SFRH/BSAB/113784/2015) and CERIS. The authors also gratefully acknowledge project Life PAYT – “Tool to Reduce Waste in South Europe” (LIFE15 ENV/PT/000609) for support.

References

  1. 1.
    European Commission: Assessment of Separate Collection Schemes in the 28 Capitals of the EU (070201/ENV/2014/691401/SFRA/A2)—Final Report (2015)Google Scholar
  2. 2.
    EPRS: Closing the loop—new circular economy package: briefing. EPRS (2016)Google Scholar
  3. 3.
    Sha’Ato, R., Aboho, S.Y., Oketunde, F.O., Eneji, I.S., Unazi, G., Agwa, S.: Survey of solid waste generation and composition in a rapidly growing urban area in Central Nigeria. Waste Manag. 27, 352–358 (2007).  https://doi.org/10.1016/j.wasman.2006.02.008 CrossRefGoogle Scholar
  4. 4.
    Oliveira, V., Sousa, V., Vaz, J.M., Dias-Ferreira, C.: Model for the separate collection of packaging waste in Portuguese low-performing recycling regions. J. Environ. Manag. 216, 13–24 (2018).  https://doi.org/10.1016/j.jenvman.2017.04.065 CrossRefGoogle Scholar
  5. 5.
    Wei, Y., Xue, Y., Yin, J., Ni, W.: Prediction of Municipal Solid Waste Generation in China By Multiple Linear Regression Method1. Int. J. Comput. Appl. (2013).  https://doi.org/10.2316/Journal.202.2013.3.202-3898 Google Scholar
  6. 6.
    Xu, L., Gao, P., Cui, S., Liu, C.: A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Manag. 33, 1324–1331 (2013).  https://doi.org/10.1016/j.wasman.2013.02.012 CrossRefGoogle Scholar
  7. 7.
    Abbasi, M., Abduli, M.A., Omidvar, B., Baghvand, A.: Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environ. Prog. Sustain. Energy 33, 220–228 (2014).  https://doi.org/10.1002/ep.11747 CrossRefGoogle Scholar
  8. 8.
    Noori, R., Karbassi, A., Salman Sabahi, M.: Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. J. Environ. Manag. 91, 767–771 (2010).  https://doi.org/10.1016/j.jenvman.2009.10.007 CrossRefGoogle Scholar
  9. 9.
    Kannangara, M., Dua, R., Ahmadi, L., Bensebaa, F.: Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Manag. 74, 3–15 (2018).  https://doi.org/10.1016/j.wasman.2017.11.057 CrossRefGoogle Scholar
  10. 10.
    Oliveira, V., Sousa, V., Dias-Ferreira, C.: Artificial neural network modelling of the amount of separately-collected household packaging waste. J. Clean. Prod. 210, 401–409 (2019).  https://doi.org/10.1016/j.jclepro.2018.11.063 CrossRefGoogle Scholar
  11. 11.
    Abbasi, M., Abduli, M.A., Omidvar, B., Baghvand, A.: Forecasting municipal solid waste generation by hybrid support sector machine and partial least square model. Int. J. Environ. Res. 7, 27–38 (2013).  https://doi.org/10.22059/IJER.2012.583 Google Scholar
  12. 12.
    Abbasi, M., Hanandeh, A., El: Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag. 56, 13–22 (2016).  https://doi.org/10.1016/j.wasman.2016.05.018 CrossRefGoogle Scholar
  13. 13.
    Noori, R., Abdoli, M.A., Ghasrodashti, A.A., Ghazizade, M.J.: Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad. Environ. Sci. Technol. 28, 249–258 (2009).  https://doi.org/10.1002/ep.10317 Google Scholar
  14. 14.
    Tonjes, D.J., Greene, K.L.: A review of national municipal solid waste generation assessments in the USA. Waste Manag. Res. 30, 758–771 (2012).  https://doi.org/10.1177/0734242X12451305 CrossRefGoogle Scholar
  15. 15.
    Abdoli, M.A., Nezhad, M.F., Sede, R.S., Behboudian, S.: Longterm forecasting of solid waste generation by the artificial neural networks. Environ. Prog. Sustain. Energy. 31, 628–636 (2012).  https://doi.org/10.1002/ep.10591 CrossRefGoogle Scholar
  16. 16.
    Vapnik, V.: Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  17. 17.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1988)zbMATHGoogle Scholar
  18. 18.
    Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing. 55, 307–319 (2003).  https://doi.org/10.1016/S0925-2312(03)00372-2 CrossRefGoogle Scholar
  19. 19.
    Oliveira, V., Sousa, V., Dias-Ferreira, C.: Dataset of socio-economic and waste collection indicators for Portugal at municipal level. Data Brief. 22, 658–661 (2019).  https://doi.org/10.1016/j.dib.2018.12.069 CrossRefGoogle Scholar
  20. 20.
    Huber, P.J.: Robust statistics. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science. Springer, Heidelberg, pp. 1248–1251 (2011)CrossRefGoogle Scholar
  21. 21.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  22. 22.
    Vapnik, V.N., Chervonenkis, A.Y.: On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Appl. 16(2), 264–279 (1971)CrossRefzbMATHGoogle Scholar
  23. 23.
    Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.CERIS, Department of Civil Engineering, Architecture and GeoResourcesTecnico Lisboa - ISTLisbonPortugal
  2. 2.Department of Civil EngineeringUniversity of AveiroAveiroPortugal
  3. 3.Research Centre for Natural Resources, Environment and Society (CERNAS)Polytechnic Institute of CoimbraCoimbraPortugal
  4. 4.Materials and Ceramic Engineering Department, CICECOUniversity of AveiroAveiroPortugal
  5. 5.Universidade Aberta de LisboaLisboaPortugal

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