Comparison of neural network and principal component-regression analysis to predict the solid waste generation in Tehran

ABSTRACT

Municipal solid waste [MSW] is the natural result of human activities. MSW generation modeling is of prime importance in designing and programming municipal solid waste management system. This study tests the short-term prediction of waste generation by artificial neural network [ANN] and principal component-regression analysis. Two forecasting techniques are presented in this paper for prediction of waste generation [WG]. One of them, multivariate linear regression [MLR], is based on principal component analysis [PCA]. The other technique is ANN model. For ANN, a feed-forward multi-layer perceptron was considered the best choice for this study. However, in this research after removing the problem of multicolinearity of independent variables by PCA, an appropriate model [PCA-MLR] was developed for predicting WG. Correlation coefficient [R] and average absolute relative error [AARE] in ANN model obtained as equal to 0.837 and 4.4% respectively. In comparison whit PCA-MLR model [R= 0.445, MARE= 6.6%], ANN model has a better results. However, threshold statistic error is done for the both models in the testing stage that the maximum absolute relative error [ARE] for 50% of prediction is 3.7% in ANN model but it is 6.2% for PCA-MLR model. Also we can say that the maximum ARE for 90% of prediction in testing step of ANN model is about 8.6% but it is 10.5% for PCA-MLR model. The ANN model has better results in comparison with the PCA-MLR model therefore this model is selected for prediction of WG in Tehran