Development of a hybrid model to predict construction and demolition waste: China as a case study

Song, Y; Wang, Y; Liu, F; Zhang, Y

Development of a hybrid model to predict construction and demolition waste: China as a case study

Song, Y Wang, Y Liu, F

Zhang, Y

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Publication Type:: Journal Article
Citation:: Waste Management, 2017, 59 pp. 350 - 361
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Song, Y	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Liu, F https://orcid.org/0000-0002-5005-9129	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.available	2016-10-08	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Waste Management, 2017, 59 pp. 350 - 361	en_US
dc.identifier.issn	0956-053X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124153
dc.description.abstract	© 2016 Elsevier Ltd Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers.	en_US
dc.relation.ispartof	Waste Management	en_US
dc.relation.isbasedon	10.1016/j.wasman.2016.10.009	en_US
dc.subject.classification	Environmental Sciences	en_US
dc.subject.mesh	Industrial Waste	en_US
dc.subject.mesh	Housing	en_US
dc.subject.mesh	Refuse Disposal	en_US
dc.subject.mesh	Waste Management	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Construction Materials	en_US
dc.subject.mesh	China	en_US
dc.subject.mesh	Construction Industry	en_US
dc.title	Development of a hybrid model to predict construction and demolition waste: China as a case study	en_US
dc.type	Journal Article
utslib.citation.volume	59	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

© 2016 Elsevier Ltd Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers.

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http://hdl.handle.net/10453/124153