title: A two-stage support-vector-regression optimization model for municipal solid waste management - a case study of Beijing, China.
abstract: In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands.
authors.affiliation: MOE Key Laboratory of Regional Energy Systems Optimization, S-C Energy and Environmental Research Academy, North China Electric Power University, Beijing 102206, China. daichao321@gmail.com
authors.forename: C
authors.initials: C
authors.lastname: Dai
authors.forename: Y P
authors.initials: YP
authors.lastname: Li
authors.forename: G H
authors.initials: GH
authors.lastname: Huang
mesh_terms: D002681:China; D002958:City Planning; D008962:Models, Theoretical; D012037:Refuse Disposal / Q000379:methods*; D012044:Regression Analysis