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ScientificWorldJournal. 2014;2014:834357. doi: 10.1155/2014/834357. Epub 2014 Jun 30.

Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction.

Author information

1
Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China ; Graduate School, Chinese Academy of Sciences, Beijing 100049, China ; Changjiang River Scientific Research Institute, Wuhan 430010, China.
2
Center for Geospatial Research, Department of Geography, The University of Georgia, Athens, GA 30602, USA.
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
4
Changjiang River Scientific Research Institute, Wuhan 430010, China.
5
Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China ; Graduate School, Chinese Academy of Sciences, Beijing 100049, China.

Abstract

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%.

PMID:
25301508
PMCID:
PMC4181496
DOI:
10.1155/2014/834357
[Indexed for MEDLINE]
Free PMC Article

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