<scp>CAPTURE</scp> —Computational Analysis and Predictive Techniques for Urban Resource Efficiency

ABSTRACT Municipal waste management (MWM) poses significant challenges in the context of rapid urbanisation and population growth. Accurate forecasting of waste production is crucial for designing sustainable waste management strategies. However, traditional forecasting methods often struggle to capture the complexities of waste generation dynamics. This paper proposes a novel methodology leveraging deep learning techniques to forecast municipal waste production. By harnessing the power of deep neural networks, our approach transcends the limitations of conventional models, providing more accurate and impactful predictions. We integrate heterogeneous data sources, including demographic and territorial information, into a comprehensive graph representation of municipalities. Graph Neural Networks are then employed to extract intricate spatial and temporal patterns from the graph structure. Empirical validation through a case study in the Apulia region demonstrates the effectiveness of our methodology in furnishing accurate forecasts for waste production. Our framework is adaptable and scalable, making it suitable for application across diverse geographical areas. This research contributes to advancing waste management practices by providing stakeholders with actionable insights for informed decision‐making.