Advanced predictive modeling of municipal solid waste management using robust machine learning

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Rising urbanization and technology have increased municipal solid waste (MSW), demanding advanced predictive models for effective municipal solid waste management (MSWM).
• Traditional waste management approaches are often reactive, lacking the foresight needed for sustainable and efficient operations.
• Machine learning (ML) offers potential for developing proactive MSWM strategies.

Purpose of the Study:

• To integrate and evaluate machine learning (ML) techniques, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Multilayer Perceptrons (MLP), and Logistic Regression (LR), for optimizing MSWM forecasting.
• To pioneer the application of CNN for MSWM prediction, addressing a gap in current research.
• To enhance strategic planning in MSWM through accurate waste generation predictions.

Main Methods:

• A structured nine-step workflow was employed, including data collection, preprocessing, model development, and validation.
• A Kaggle-sourced dataset of 4,341 records with 20 variables (e.g., population density, waste composition) was utilized for training, testing, and validation.
• Performance was assessed using statistical metrics such as R-squared (R²) and Root Mean Squared Error (RMSE).

Main Results:

• Convolutional Neural Networks (CNN) demonstrated superior accuracy across training, testing, and validation datasets, achieving R² values of 0.999, 0.996, and 0.996, respectively.
• CNN outperformed SVM, MLP, and LR in predicting municipal solid waste generation.
• The model's ability to handle nonlinear relationships and data irregularities led to precise predictions, improving route optimization and resource allocation.

Conclusions:

• Machine learning, particularly CNN, offers a transformative approach to MSWM, enabling a shift from reactive to proactive management.
• Accurate waste generation forecasting enhances operational efficiency and supports environmental sustainability goals.
• The study highlights the novelty of using CNN for MSWM prediction and its regularization strategies to prevent overfitting.