A Machine Learning Framework For Predictive Waste Management Optimization In Smart Cities

Abstract

Urban waste generation is rising rapidly worldwide (2.01 billion tonnes in 2016 to an expected 3.8 billion tonnes by 2050). Traditional waste collection methods struggle to handle this growth efficiently. We propose a comprehensive AI-driven framework combining Internet of Things (IoT) sensors, data analytics, and machine learning (ML) to predict waste generation and optimize collection routes in smart cities. Our layered architecture uses real-time bin-level data (fill levels, location, usage patterns) to forecast waste volumes via regression models (e.g. XGBoost, random forests, neural networks) and adjust collection schedules dynamically. We validate the framework with an open Smart Bin dataset and simulated city scenarios. The best ML model (XGBoost) achieves a root mean squared error (RMSE) of 4.10 tonnes on test data, outperforming linear regression and random forest (Table 1). Dynamic route optimization using these predictions reduces collection distance and fuel consumption by over 30%, consistent with prior studies. We discuss design details (sensors, data pipeline, ML algorithms), present evaluation results (tables and graphs), and highlight deployment challenges (data quality, privacy, scalability). The proposed framework significantly improves waste management efficiency and sustainability in smart city deployments.

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