Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam

Abstract

The main aim of this work was to compare six machine learning (ML) - based models to predict the municipal solid waste (MSW) generation from selected residential areas of Vietnam. The input data include eight variables that cover the economy, demography, consumption and waste generation characteristics of the study area. The model simulation results showed that the urban population, average monthly consumption expenditure, and total retail sales were the most influential variables for MSW generation. Among the ML models, the random forest (RF), and k-nearest neighbor (KNN) algorithms show good predictive ability of the training data (80% of the data), with an R2 value > 0.96 and a mean absolute error (MAE) of 121.5–125.0 for the testing data (20% of the data). The developed ML models provided reliable forecasting of the data on MSW generation that will help in the planning, design and implementation of an integrated solid waste management action plan for Vietnam. The limitations of this work may be the heterogeneity of the dataset, such as the lack of data from lower administrative units in the country. In such cases, the predictive ML algorithm can be updated and re-trained in the future when the reliable data is added.