Forecasting E-Waste in Presence of Limited Data
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Authors
Duman, Gazi Murat
Issue Date
2019-03-29
Type
Other
Language
en_US
Keywords
Grey modeling , Electronic waste , Particle swarm optimization
Alternative Title
Abstract
Electronic waste (E-waste) has emerged as one of the fastest growing municipal solid waste streams in the United States due to rapid changes in technology and increasing consumer demand. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations. The literature offers various methodologies focusing on prediction of e-waste generation. Among these, Grey Modeling (GM) approach has drawn attention due to its ability to provide meaningful results with utilizing relatively small-sized data. In order to improve the overall success rate of the approach, several GM-based models have been developed over the years. The performance of these models, however, heavily rely on the parameters used with no established consensus regarding the suitable criteria for better accuracy. This study presents a novel GM approach improved by Particle Swarm Optimization (PSO). A case study utilizing Washington State e-waste data is provided to demonstrate the comparative analysis proposed in the study.