Forecasting Automotive Waste Generation Using Short Data Sets: Case Study of Lithuania

: cc. 11-18
Kaunas University of Technology
Kaunas University of Technology
Kaunas University of Technology

There were 1.83 million cars and average passenger car age was 18 years in Lithuania in 2013. Increasing number of cars has an insignificant effect on car age change but it is contrary to automotive waste, both hazardous and non-hazardous, that accumulates during vehicle exploitation and after it ends. The aim of this study was to assess different mathematical modelling methods abilities to forecast non-hazardous and hazardous automotive waste generation. Artificial neural networks, multiple linear regression, partial least squares, support vector machines, nonparametric regression and time series methods were used in this research. Results revealed that nearly perfect theoretical results in both cases can be reached by smoothing splines and other nonparametric regression methods. It is very doubtful that results would be so precise using data outside of currently used data set range and due to this reason further testing using 2014–2015 data is needed.

[1] Abbasi, M. Abdul, M. A. Omidvar, B. Baghvand, A. International Journal of Environmental Research, 2013, 7(1), pp. 27–38.

[2] Abdol, M. A. Nezhad, M. F. Sede, R. S. Behboudian, S. Wiley Online Library, 2011, DOI 10.1002/ep.10591.

[3] Agency of Environmental protection. lang=en(accessed June 29, 2016)

[4] Chaerul, M. Tanaka, M. Shekdar, AV. Waste Management, 2008, 28: 442–449.

[5] Cortes, C., Vapnik, V. Support-vector network. Machine Learning, 1995, 20, pp. 1–25.

[6] Denafas, G. Ruzgas, T. Martuzevičius, D. Shmarin, S. Hoffmann, M. and others. Resources, Conservation and Recycling, 2014, 89, pp. 22–33.

[7] Eleyan, D, Al-Khatib, I. A., Garfield, J. Waste Management & Research, 2013, 31(10):986–95.

[8] European Commission Directorate – General for Energy and Transport. Statistical pocket book 2004–2013 (accessed June 29, 2016)

[9] Government institution “Regitra”. (accessed June 29, 2016)

[10] Hastie, T. J. and Tibshirani, R. J. Generalized Additive Models. New York: Chapman & Hall, 1990.

[11] Heddam, S. Environmental processes, 2016, 3:525–536.

[12] Huawen, L. Zongjie, M. et al. International Journal of Machine Learning & Cybernetics, 2016.

[13] Jahandideh, S. Jahandideh, S. Asadabadi, E.B. Askarian, M. Movahedi, M.M. Hosseini, S. Jahandided, M. Waste Management, 2009, 29, pp. 2874–2879.

[14] Liutkevičiūtė, V. Neparametrinių regresinių metodų lyginamasis tyrimas. Kaunas, 2014.

[15] Noori, R. Abdoli, M.A. Farokhnia, A. Abbasi, M. Expert Systems with Application, 2009, 36, pp. 9991–9999.

[16] Rimaitytė, I. Ruzgas, T. Denafas, G. Račys, V. Martuzevičius, D. Waste Management & Research, 2012, 30(1), pp. 89–98.

[17] SAS user guide. (accessed June 29, 2016).

[18] Shumway, R., Stoffer, D. Time Series Analysis and Its Applications. With R examples. Springer texts in statistics, 2011.

[19] Yetilmezsoy, K. Ozkaya, B. Cakmakci, M. Artificial intelligence-based prediction models for environmental engineering. Neural Network World, 2011.

[20] Meyer, D. Support vector mashines. The interface to libsvm in package e1071. 2014.