A hybrid procedure for MSW generation forecasting at multiple time scalesin Xiamen City, China
Lilai Xua, Peiqing Gaoc, Shenghui Cuia*, Chun Liuc
Accurate forecasting of municipal solid waste (MSW) generation is crucial and fundamental for the plan-ning, operation and optimization of any MSW management system. Comprehensive information onwaste generation for month-scale, medium-term and long-term time scales is especially needed, consid-ering the necessity of MSW management upgrade facing many developing countries. Several existingmodels are available but of little use in forecasting MSW generation at multiple time scales. The goalof this study is to propose a hybrid model that combines the seasonal autoregressive integrated movingaverage (SARIMA) model and grey system theory to forecast MSW generation at multiple time scaleswithout needing to consider other variables such as demographics and socioeconomic factors. To demon-strate its applicability, a case study of Xiamen City, China was performed. Results show that the model isrobust enough to ﬁt and forecast seasonal and annual dynamics of MSW generation at month-scale, med-ium- and long-term time scales with the desired accuracy. In the month-scale, MSW generation in Xia-men City will peak at 132.2 thousand tonnes in July 2015 – 1.5 times the volume in July 2010. In themedium term, annual MSW generation will increase to 1518.1 thousand tonnes by 2015 at an averagegrowth rate of 10%. In the long term, a large volume of MSW will be output annually and will increaseto 2486.3 thousand tonnes by 2020 – 2.5 times the value for 2010. The hybrid model proposed in thispaper can enable decision makers to develop integrated policies and measures for waste managementover the long term.