Comparison of Neural Network and Principal Component-Regression Analysis to Predict the Solid Waste Generation in Tehran
"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 artificia...
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Tehran University of Medical Sciences,
2009-03-01T00:00:00Z.
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LEADER | 00000 am a22000003u 4500 | ||
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001 | doaj_1e6b67d3a6094bc4a4da9a36eb51d1b7 | ||
042 | |a dc | ||
100 | 1 | 0 | |a R Noori |e author |
700 | 1 | 0 | |a MA Abdoli |e author |
700 | 1 | 0 | |a M Jalili Ghazizade |e author |
700 | 1 | 0 | |a R Samieifard |e author |
245 | 0 | 0 | |a Comparison of Neural Network and Principal Component-Regression Analysis to Predict the Solid Waste Generation in Tehran |
260 | |b Tehran University of Medical Sciences, |c 2009-03-01T00:00:00Z. | ||
500 | |a 2251-6085 | ||
520 | |a "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. | ||
546 | |a EN | ||
690 | |a Prediction of waste generation | ||
690 | |a Artificial neural network | ||
690 | |a Multivariable linear regression | ||
690 | |a Principle component analysis | ||
690 | |a Public aspects of medicine | ||
690 | |a RA1-1270 | ||
655 | 7 | |a article |2 local | |
786 | 0 | |n Iranian Journal of Public Health, Vol 38, Iss 1, Pp 74-84 (2009) | |
787 | 0 | |n http://journals.tums.ac.ir/PdfMed.aspx?pdf_med=/upload_files/pdf/12874.pdf&manuscript_id=12874 | |
787 | 0 | |n https://doaj.org/toc/2251-6085 | |
856 | 4 | 1 | |u https://doaj.org/article/1e6b67d3a6094bc4a4da9a36eb51d1b7 |z Connect to this object online. |