Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction
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 p...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/834357 |
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| _version_ | 1841524540170043392 |
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| author | Jingwei Song Jiaying He Menghua Zhu Debao Tan Yu Zhang Song Ye Dingtao Shen Pengfei Zou |
| author_facet | Jingwei Song Jiaying He Menghua Zhu Debao Tan Yu Zhang Song Ye Dingtao Shen Pengfei Zou |
| author_sort | Jingwei Song |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-55dc500351b74ab18625d35e7fecc52c |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-55dc500351b74ab18625d35e7fecc52c2025-02-03T05:53:02ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/834357834357Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation PredictionJingwei Song0Jiaying He1Menghua Zhu2Debao Tan3Yu Zhang4Song Ye5Dingtao Shen6Pengfei Zou7Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaCenter for Geospatial Research, Department of Geography, The University of Georgia, Athens, GA 30602, USASchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaChangjiang River Scientific Research Institute, Wuhan 430010, ChinaChangjiang River Scientific Research Institute, Wuhan 430010, ChinaChangjiang River Scientific Research Institute, Wuhan 430010, ChinaChangjiang River Scientific Research Institute, Wuhan 430010, ChinaKey Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaA 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%.http://dx.doi.org/10.1155/2014/834357 |
| spellingShingle | Jingwei Song Jiaying He Menghua Zhu Debao Tan Yu Zhang Song Ye Dingtao Shen Pengfei Zou Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction The Scientific World Journal |
| title | Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction |
| title_full | Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction |
| title_fullStr | Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction |
| title_full_unstemmed | Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction |
| title_short | Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction |
| title_sort | simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction |
| url | http://dx.doi.org/10.1155/2014/834357 |
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