Comparison of Neural Network and Principal Component-Regression Analysis to Predict the Solid Waste Generation in Tehran

Autor: Roohollah Noori, Abdoli, M. A., Ghazizade, M. J., Samieifard, R.
Jazyk: angličtina
Rok vydání: 2009
Předmět:
Zdroj: Iranian Journal of Public Health, Vol 38, Iss 1, Pp 74-84 (2009)
Iranian Journal of Public Health, Vol 38, Iss 1 (2009)
Scopus-Elsevier
ISSN: 2251-6085
Popis: "nBackground: 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."nMethods: 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."nResults: 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."nConclusion: The ANN model has better results in comparison with the PCA-MLR model therefore this model is selected for prediction of WG in Tehran.  
Databáze: OpenAIRE
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