Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting



The prediction of municipal solid waste generation (MSWG) plays an important role in a solid waste management system. However, achieving the anticipated prediction accuracy with regard to the nonhomogeneous nature of waste and effect of various and out of control factors on MSWG is quite challenging. In this article, support vector machine (SVM), one of the artificial intelligence techniques, and hybrid of wavelet transform (WT) and support vector machine (WT-SVM) are used to predict weekly time series of MSWG in Tehran and Mashhad cites during the period of January 2006–December 2011. To improve the performance of SVM model, considering the influence of noise and the disadvantages of traditional noise eliminating technologies, the wavelet denoising method is applied to reduce or eliminate the noise in MSWG time series.

Since Data-driven models such as SVM involve potential of uncertainty that is difficult to quantify, uncertainty determination is one of important gaps observed in SVM results analysis. Therefore, Monte Carlo method was used to analyze uncertainty of the model results.

Results showed both models could precisely predict MSWG in Tehran and Mashhad cites. However, the preprocessing of input variables by WT led to develop a more accurate model for prediction of weekly MSWG in both cities. The uncertainty analysis also verified that the WT-SVM model had more robustness than SVM and had a lower sensitivity to change of input variables. © 2013 American Institute of Chemical Engineers Environ Prog, 33: 220–228, 2014