title: Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction.
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%.
authors.affiliation: 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.
authors.forename: Jingwei
authors.initials: J
authors.lastname: Song
authors.affiliation: Center for Geospatial Research, Department of Geography, The University of Georgia, Athens, GA 30602, USA.
authors.forename: Jiaying
authors.initials: J
authors.lastname: He
authors.affiliation: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
authors.forename: Menghua
authors.initials: M
authors.lastname: Zhu
authors.affiliation: Changjiang River Scientific Research Institute, Wuhan 430010, China.
authors.forename: Debao
authors.initials: D
authors.lastname: Tan
authors.affiliation: Changjiang River Scientific Research Institute, Wuhan 430010, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Changjiang River Scientific Research Institute, Wuhan 430010, China.
authors.forename: Song
authors.initials: S
authors.lastname: Ye
authors.affiliation: Changjiang River Scientific Research Institute, Wuhan 430010, China.
authors.forename: Dingtao
authors.initials: D
authors.lastname: Shen
authors.affiliation: 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.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Zou
mesh_terms: D005544:Forecasting / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D062611:Solid Waste / Q000706:statistics & numerical data*; D060388:Support Vector Machine
chemical_list: D062611:Solid Waste