Estimation of electronic waste using optimized multivariate grey models

Rapid and revolutionary changes in technology and rising demand for consumer electronics have led to staggering rates of accumulation of electrical and electronic equipment waste, viz., WEEE or e-waste. Consequently, e-waste has become one of the fastest growing municipal solid waste streams in the United States making its efficient management crucial in supporting the efforts to create and sustain green cities. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations that have become more complicated and unpredictable. Early work focusing on prediction of e-waste generation includes a wide range of methodologies. Among these, grey forecasting models have drawn attention due to their capability to provide meaningful results with relatively small-sized or limited data. The performance of grey models heavily rely on their parameters. The purpose of this study is to present a novel forecasting technique for e-waste predictions with multiple inputs in presence of limited historical data. The proposed nonlinear grey Bernoulli model with convolution integral NBGMC(1,n) improved by Particle Swarm Optimization (PSO) demonstrates superior accuracy over alternative forecasting models. The proposed model and its findings are delineated with the help of a case study utilizing Washington State e-waste data. The results indicate that population density has a major impact on the generated e-waste followed by household income level. The findings also show that the e-waste generation forms a saturated distribution in Washington State. These results can help decision makers plan for more effective reverse logistics infrastructures that would ensure proper collection, recycling and disposal of e-waste.