School of Economics & Management, Xidian University, Xian 710126, China; Shaanxi Soft Science Institute of Information and Digital Economy, Xian 71012.6, China.
School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China.
Waste Manag. 2021 Feb 1;120:828-838. doi: 10.1016/j.wasman.2020.11.006. Epub 2020 Dec 4.
Waste electrical and electronic equipment (viz., WEEE or e-waste) is the fastest-growing type of hazardous solid waste in the worldwide. The accurate prediction of the amount of e-waste might help improve the efficiency of e-waste disposal. In this study, a novel decomposition-ensemble-based hybrid forecasting methodology that integrates variational mode decomposition (VMD), exponential smoothing model (ESM), and grey modeling (GM) methods (named VMD-ESM-GM) is proposed for e-waste quantity prediction. For verification purposes, sample data from Washington State, US, and UK Environment Agency are analyzed. Compared to benchmark models, the proposed VMD-ESM-GM methodology not only obtains a satisfactory prediction result for e-waste data but also predicts the future fluctuation trend of e-waste. These results indicate that the proposed VMD-ESM-GM methodology based on the decomposition-ensemble principle is a suitable model for the prediction of the e-waste quantity and could help decision-makers develop both e-waste recycling plans and circular economy plans.
废弃电气电子设备（即 WEEE 或电子废物）是全球增长最快的危险固体废物类型。准确预测电子废物的数量可能有助于提高电子废物处理的效率。在这项研究中，提出了一种新颖的基于分解-集成的混合预测方法，该方法结合了变分模态分解 (VMD)、指数平滑模型 (ESM) 和灰色模型 (GM) 方法（称为 VMD-ESM-GM），用于电子废物数量预测。为了验证目的，分析了来自美国华盛顿州和英国环境署的样本数据。与基准模型相比，所提出的 VMD-ESM-GM 方法不仅对电子废物数据进行了令人满意的预测，而且还预测了电子废物的未来波动趋势。这些结果表明，基于分解-集成原理的提出的 VMD-ESM-GM 方法是一种适合电子废物数量预测的模型，可以帮助决策者制定电子废物回收计划和循环经济计划。
