Artificial neural network modelling of the amount of separately-collected household packaging waste
authors Oliveira, V; Sousa, V; Dias-Ferreira, C
nationality International
journal JOURNAL OF CLEANER PRODUCTION
author keywords Municipal solid waste; ANN; Genetic algorithm; Regression model; Urban waste; Recycling
keywords GENERATION; PREDICTION; MSW
abstract This work develops an artificial neural network (ANN) model using genetic algorithms to estimate the annual amount (kg/inhabitant/year) of separately-collected household packaging waste. The ANN model comprises one input layer, one hidden layer with seven neurons and one output layer. Ten variables affecting the amount of separately-collected packaging waste were identified and used in the ANN model. These variables are related to the level of education of the population, the size and level of urbanisation of the municipality, social aspects related to poverty and economic power and factors intrinsic to the waste collection service. A comparison between ANN and regression models for the estimation of packaging waste is also carried out The performance of the proposed ANN model for a data set of 42 municipalities located in the centre of Portugal, measured by the R-2. is 0.98. This value is 34% higher than the best regression model applied to the same data set (R-2 = 0.73), indicating that ANN has a significantly higher explanatory power than traditional regression techniques. Another advantage is that ANN is not as sensitive to outliers as regression. However, ANN is more complex, has a higher number of variables, and the model development and interpretation of the results are more difficult Nevertheless, the higher performance of ANN makes it a valuable tool in the definition of strategies to increase recycling and achieve circular economy goals. (C) 2018 Elsevier Ltd. All rights reserved.
publisher ELSEVIER SCI LTD
issn 0959-6526
year published 2019
volume 210
beginning page 401
ending page 409
digital object identifier (doi) 10.1016/j.jclepro.2018.11.063
web of science category Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences
subject category Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology
unique article identifier WOS:000456762600036
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journal analysis (jcr 2017):
journal impact factor 5.651
5 year journal impact factor 6.352
category normalized journal impact factor percentile 87.287
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