Municipal Solid Waste (MSW) Management Prediction Through Machine Learning Models: An Ensemble Tree Regressor Analysis

MSW management is one of the critical global challenges, especially in the context of rapid urbanization and economic development. This study examines the factors that influence MSW generation and the potential of machine learning techniques to predict future trends. Using a dataset covering 196 countries and 13 regions, we analyze key variables such as population, GDP per capita, urbanization rate, and Human Development Index (HDI). In preprocessing, missing values were managed and new features like MSW and population growth rates, waste intensity, and HDI-adjusted waste were engineered to capture the complex relationships influencing MSW generation. An Extra Trees Regressor model was used for future prediction, achieving an R² value of 0.89, thereby proving its excellent performance in predictive results. Our results demonstrate how economic development and urbanization contribute to waste generation trends, with the AI models further providing insightful implications of such changes. The need for data-informed approaches for designing sustainable systems of waste management is underscored by this study, highlighting that socio-economic drivers and technological leaps must be used in concert with each other in order to adequately address the challenges of increasing wastes. The findings offer a basis for future policies and actions that would aim at managing waste effectively in an increasingly urbanized world.