Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad

Abstract

Quantity prediction of municipal solid waste (MSW) is crucial for design and programming municipal solid waste management system (MSWMS). Because effect of various parameters on MSW quantity and its high fluctuation, prediction of generated MSW is a difficult task that can lead to enormous error. The works presented here involve developing an improved support vector machine (SVM) model, which combines the principal component analysis (PCA) technique with the SVM to forecast the weekly generated waste of Mashhad city. In this study, the PCA technique was first used to reduce and orthogonalize the original input variables (data). Then these treated data were used as new input variables in SVM model. This improved model was evaluated by using weekly time series of waste generation (WG) and the number of trucks that carry waste in week of t. These data have been collected from 2005 to 2008. By comparing the predicted WG with the observed data, the effectiveness of the proposed model was verified. Therefore, in authors' opinion, the model presented in this article is a potential tool for predicting WG and has advantages over the traditional SVM model.

Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic

“…In this method, the information of input variables will present with minimum losses in PCs (Helena et al, 2000). Details for mastering the art of PCA is published elsewhere (Noori, Abdoli, Ameri, & Jalili-Ghazizade, 2009b;Noori, Abdoli, Jalili-Ghazizade, & Samifard, 2009c;Noori, Kerachian, Khodadadi, & Shakibayinia, 2007;Tabachnick & Fidell, 2001;Wackernagel, 1995).…”

A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction

“…Based on this fact, we also include sARIMA as a contrast method in this article. As for nonlinear models, ANN (Zade and Noori, 2008;Noori et al, 2010) and SVM (Noori et al, 2009;Abbasi et al, 2012Abbasi et al, , 2013 are the most commonly used model for weekly MSW data. This article only chose two typical nonlinear ANN and SVM models (nonlinear autoregressive with exogenous input [NARX] and PLS-SVM) as contrast models because we focused on discussing the performance of the basic model structure and the preprocessing method (wavelet transform and principal component transform) proposed in these articles can be implemented on chaotic model in further discussion.…”

Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

“…The corresponding eigenvectors b 1 , b 2 , …, b r give the coefficients of the Y variables for canonical variates. The coefficients of linear combination of X variables (U i ) and the ith canonical variate for the X variables are given by the elements of the a i vector [19].…”