A two-stage support-vector-regression optimization model for municipal solid waste management – A case study of Beijing, China

• Published in: Journal of environmental management Vol. 92; no. 12; pp. 3023 - 3037
• Main Authors: Dai, C., Li, Y.P., Huang, G.H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2011; Elsevier; Academic Press Ltd
• Subjects: Accuracy; Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; China; City Planning; Conservation, protection and management of environment and wildlife; Criteria; Environmental management; Environmental policy; Forecasts; Fundamental and applied biological sciences. Psychology; General aspects; Integer programming; Interactive systems; Interval; Kernel functions; Kernels; Linear programming; Management; Management techniques; Mathematical models; Medically important nuisances and vectors, pests of stored products and materials: population survey and control; Mixed integer; Modelling; Models, Theoretical; Multilayers; Municipal solid waste; Municipal waste management; Optimization; Optimization models; Parameter optimization; Performance prediction; Planning; Planning methods; Polynomials; Radial basis function; Refuse Disposal - methods; Regression Analysis; Regression models; Simulation; Solid waste management; Solid wastes; Support-vector-regression; Uncertainty; Urban services; Vectors. Intermediate hosts; Waste disposal; Waste management; Waste treatment; Wastes; China; Beijing China; China, People's Rep., Beijing; Uncertainty; Support-vector-regression; Municipal solid waste; Management; Planning; Optimization; Interval; Urban waste; Regression; Environmental management; Case study; Waste management; Beijing China; Regression model; Solid waste; Vector
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2011.06.038

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. ► A two-stage support-vector-regression optimization model (TSOM) is developed. ► TSOM is applied to municipal solid waste (MSW) management in the urban districts of Beijing. ► Prediction performances of four kernel functions in SVR model are analyzed. ► It can reflect dynamic, interactive, and uncertain characteristics of MSW management system. ► Results can identify effects of critical factors on waste-generation and -management practice.