| Autor: |
Coskuner G; Department of Chemical Engineering, College of Engineering, University of Bahrain, Bahrain., Jassim MS; Department of Chemical Engineering, College of Engineering, University of Bahrain, Bahrain., Zontul M; Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Arel University, Turkey., Karateke S; Department of Mathematics and Computer Science, Faculty of Science and Letters, Istanbul Arel University, Turkey. |
| Jazyk: |
angličtina |
| Zdroj: |
Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA [Waste Manag Res] 2021 Mar; Vol. 39 (3), pp. 499-507. Date of Electronic Publication: 2020 Jun 25. |
| DOI: |
10.1177/0734242X20935181 |
| Abstrakt: |
Reliable prediction of municipal solid waste (MSW) generation rates is a significant element of planning and implementation of sustainable solid waste management strategies. In this study, the multi-layer perceptron artificial neural network (MLP-ANN) is applied to verify the prediction of annual generation rates of domestic, commercial and construction and demolition (C&D) wastes from the year 1997 to 2016 in Askar Landfill site in the Kingdom of Bahrain. The proposed robust predictive models incorporated selected explanatory variables to reflect the influence of social, demographical, economic, geographical and touristic factors upon waste generation rates (WGRs). The Mean Squared Error (MSE) and coefficient of determination ( R 2 ) are used as performance indicators to evaluate effectiveness of the developed models. MLP-ANN models exhibited strong accuracy in predictions with high R 2 and low MSE values. The R 2 values for domestic, commercial and C&D wastes are 0.95, 0.99 and 0.91, respectively. Our results show that the developed MLP-ANN models are effective for the prediction of WGRs from different sources and could be considered as a cost-effective approach for planning integrated MSW management systems. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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