Forecasting municipal solid waste generation and composition using machine learning and GIS techniques: A case study of Cape Coast, Ghana

Abstract

As developing countries grow and urbanize quickly, the amount of waste they produce is increasing, leading to significant challenges for waste management. This study employs machine learning techniques to predict municipal solid waste (MSW) composition and generation rates in Cape Coast, Ghana, integrating socioeconomic and geospatial variables to support the development of effective waste-to-energy (WtE) adoption strategies. The research utilized correlation analysis and three machine learning models: Linear Regression, Random Forest, and Long Short-Term Memory networks. The correlation analysis revealed strong positive relationships between population, built area, and daily waste generation (Pearson's r > 0.85), while temperature variables showed minimal correlation. Among the models evaluated, Random Forest demonstrated superior performance, achieving an R-squared score of 0.9915 and the lowest error metrics (MAE: 0.0422, MSE: 0.0077). Feature importance analysis identified population and built area as the most critical factors influencing waste generation, with importance scores of 0.508 and 0.483, respectively. These findings underscore the significant impact of urbanization on waste production and the need for integrated urban planning and waste management strategies. The results provide valuable insights for policymakers and urban planners, highlighting the necessity for waste management infrastructure to scale with urban growth. Nonetheless, the lack of gross domestic data (GDP) data limits the comprehensiveness of the analysis and may affect the forecasting accuracy. Future studies would benefit from exploring alternative economic indicators for a more comprehensive approach to waste management planning, especially in regions with scarce data. The study demonstrates the efficacy of machine learning approaches in predicting MSW dynamics, offering a robust tool for developing targeted WtE adoption strategies in rapidly urbanizing African contexts. • The study employs ML techniques to predict MSW composition and generation rates, integrating socioeconomic and geospatial variables. • Random Forest outperformed others with an R-squared score of 0.9915, indicating its effectiveness in forecasting waste generation. • The research emphasizes the importance of urbanization factors like population density and built area in influencing waste generation. • The findings underscore the need for integrated urban planning that accommodates rapid urbanization and its impact on waste production. • The study advocates for enhanced waste management infrastructure to keep pace with urban growth.

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