Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches
Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.
| Publié dans: | Waste management (New York, N.Y.). - 1999. - 74(2018) vom: 12. Apr., Seite 3-15 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2018
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| Accès à la collection: | Waste management (New York, N.Y.) |
| Sujets: | Journal Article Decision trees Forecasting Machine learning Modeling Municipal solid waste Neural networks Solid Waste |
| Résumé: | Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved. The main objective of this study was to develop models for accurate prediction of municipal solid waste (MSW) generation and diversion based on demographic and socio-economic variables, with planned application of generating Canada-wide MSW inventories. Models were generated by mapping residential MSW quantities with socio-economic and demographic parameters of 220 municipalities in the province of Ontario, Canada. Two machine learning algorithms, namely decision trees and neural networks, were applied to build the models. Socio-economic variables were derived from Canadian Census data at regional and municipal levels. A data pre-processing and integration framework was developed in Matlab® computing software to generate datasets with sufficient data quantity and quality for modeling. Results showed that machine learning algorithms can be successfully used to generate waste models with good prediction performance. Neural network models had the best performance, describing 72% of variation in the data. The approach proposed in this study demonstrates the feasibility of creating tools that helps in regional waste planning by means of sourcing, pre-processing, integrating and modeling of publically available data from various sources |
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| Description: | Date Completed 10.08.2018 Date Revised 02.12.2018 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2017.11.057 |