Department of Applied Informatics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
Department of Automation, Kaunas University of Technology, Studentu 48, Kaunas 51367, Lithuania.
Waste Manag. 2022 Mar 1;140:31-39. doi: 10.1016/j.wasman.2022.01.004. Epub 2022 Jan 13.
Forecasting municipal solid waste (MSW) generation and composition plays an essential role in effective waste management, policy decision-making and the MSW treatment process. An intelligent forecasting system could be used for short-term and long-term waste handling, ensuring a circular economy and a sustainable use of resources. This study contributes to the field by proposing a hybrid k-nearest neighbours (H-kNN) approach to forecasting municipal solid waste and its composition in the regions that experience data incompleteness and inaccessibility, as is the case for Lithuania and many other countries. For this purpose, the average MSW generation of neighbouring municipalities, as a geographical factor, was used to impute missing values, and socioeconomic factors together with demographic indicator affecting waste collected in municipalities were identified and quantified using correlation analysis. Among them, the most influential factors, such as population density, GDP per capita, private property, foreign investment per capita, and tourism, were then incorporated in the hierarchical setting of the H-kNN approach. The results showed that, in forecasting MSW generation, H-kNN achieved MAPE of 11.05%, on average, including all Lithuanian municipalities, which is by 7.17 percentage points lower than obtained using kNN. This implies that by finding relevant factors at the municipal level, we can compensate for the data incompleteness and enhance the forecasting results of MSW generation and composition.
预测城市固体废物（MSW）的产生和组成对于有效的废物管理、政策决策和 MSW 处理过程至关重要。智能预测系统可用于短期和长期废物处理，确保循环经济和资源的可持续利用。本研究通过提出一种用于预测数据不完整和不可及地区（如立陶宛和许多其他国家）的城市固体废物及其组成的混合 k-最近邻（H-kNN）方法，为该领域做出了贡献。为此，使用相邻城市的平均 MSW 产生作为地理因素来估算缺失值，并使用相关分析确定和量化影响城市收集废物的社会经济因素和人口指标。其中，人口密度、人均 GDP、私人财产、人均外国投资和旅游业等最具影响力的因素随后被纳入 H-kNN 方法的层次设置中。结果表明，在预测 MSW 产生方面，H-kNN 实现了平均 MAPE 为 11.05%，包括所有立陶宛城市，比使用 kNN 获得的结果低 7.17 个百分点。这意味着通过在市级找到相关因素，可以弥补数据不完整，并提高 MSW 产生和组成的预测结果。
