Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors

Abstract

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.

Keywords: Composition; Forecasting; K-nearest neighbours; Machine learning; Municipal solid waste.