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The Scientific World Journal
Volume 2014, Article ID 834357, 7 pages
http://dx.doi.org/10.1155/2014/834357
Research Article

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

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

Received 16 April 2014; Accepted 25 May 2014; Published 30 June 2014

Academic Editor: Guojie Zhang

Copyright © 2014 Jingwei Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Jingwei Song, Jiaying He, and Jing Zhen, “Real-Time Data Assimilation for Improving Linear Municipal Solid Waste Prediction Model: A Case Study in Seattle,” Journal Of Energy Engineering, vol. 141, no. 4, 2015. View at Publisher · View at Google Scholar
  • Cheng Cheng, Lihai Zhang, and Russell George Thompson, “Disaster waste clean-up system performance subject to time-dependent disaster waste accumulation,” Natural Hazards, 2017. View at Publisher · View at Google Scholar
  • Patchalai Anuchaivong, Dusadee Sukawat, and Anirut Luadsong, “Statistical Downscaling for Rainfall Forecasts Using Modified Constructed Analog Method in Thailand,” The Scientific World Journal, vol. 2017, pp. 1–24, 2017. View at Publisher · View at Google Scholar