Predicting the Amount of Municipal Solid Waste via Hybrid Principal Component Analysis-Artificial Neural Network Approach

Abstract

Accurate prediction of the amount of municipal solid waste (MSW) is crucial for designing and programming MSW management systems. Reliable estimation of MSW is difficult since many variables such as socio-economic characteristics, climatic factors and standard of living affect it. A number of studies used artificial neural network (ANN) to predict MSW. However, due to the large number of input variables to the ANN, it could not not perform well and generally encountered overfitting. This study takes advantage of the principal component analysis (PCA) technique to reduce the number of input variables to the ANN model in order to overcome the overfitting problem. The proposed PCA-ANN approach is used to predict the weight of MSW in the province of Mashhad, Iran. The utilized experimental data in this study are obtained from the Recycling Organization of Mashhad Municipality archive (http://www.wmo.mashhad.ir). It is found that the PCA approach can successfully decrease the number of input variables from thirteen to eight. The PCA-ANN model (with eight input variables) outperforms ANN (with thirteen input variables) and provides more accurate estimates of MSW as it mitigates the overfitting problem associated with ANN. The root-mean-square-error (RMSE) of MSW estimates reduces from 499000 Kg to 448000 Kg by using the PCA-ANN model instead of ANN.