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

background: 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. methods: 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. results: 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. conclusion: the ann model has better results in comparison with the pca-mlr model therefore this model is selected for prediction of wg in tehran.