Enhancing performance prediction of municipal solid waste generation: a strategic management

Abstract

Municipal Solid Waste Generation (MSWG) presents a significant challenge for sustainable urban development, with waste production escalating at alarming rates worldwide. To address this issue, accurate predictive models are essential for optimizing waste management strategies. This study utilizes a comprehensive dataset of 4,343 records from municipal waste management, incorporating variables such as population density, urbanization indices, and waste composition. Advanced machine learning algorithms, including Decision Trees (DT), Random Forest (RF), LightGBM, and XGBoost, are employed, with XGBoost being introduced as a novel approach for MSWG prediction. Its ability to model complex nonlinear relationships, handle missing data and outliers robustly, and prevent overfitting through advanced regularization techniques sets it apart from other models. The study finds that XGBoost outperforms the other algorithms, achieving an R 2 value of 0.985 and an RMSE of 0.056, making it the most accurate predictor of MSWG. The flexibility and scalability of XGBoost further enhance its applicability in managing diverse datasets, and its feature-ranking capability is instrumental in identifying key factors influencing waste generation. The results demonstrate that incorporating XGBoost into waste management frameworks can significantly improve resource allocation, reduce operational costs, and contribute to environmental sustainability. This approach not only advances predictive methodologies in MSWG management but also provides actionable insights for urban planners and policymakers in effectively tackling the growing waste management crisis. The findings highlight the potential of machine learning, particularly XGBoost, as a transformative tool for strategic decision-making in environmental management.

Topics & Concepts