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Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction
Joint Authors
Song, Jingwei
He, Jiaying
Zhu, Menghua
Tan, Debao
Zhang, Yu
Ye, Song
Shen, Dingtao
Zou, Pengfei
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-30
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model.
The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States.
The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models.
The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%.
Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.
American Psychological Association (APA)
Song, Jingwei& He, Jiaying& Zhu, Menghua& Tan, Debao& Zhang, Yu& Ye, Song…[et al.]. 2014. Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1051258
Modern Language Association (MLA)
Song, Jingwei…[et al.]. Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1051258
American Medical Association (AMA)
Song, Jingwei& He, Jiaying& Zhu, Menghua& Tan, Debao& Zhang, Yu& Ye, Song…[et al.]. Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1051258
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051258