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MSW-Net: A hierarchical stacking model for automated municipal solid waste classification.

Vaishnavi Jayaraman¹, Arun Raj Lakshminarayanan¹

¹Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India.

Journal of the Air & Waste Management Association (1995)

|June 20, 2024

Summary

This study introduces MSW-Net, an automated municipal solid waste (MSW) classification model. MSW-Net achieves high accuracy, improving waste management and protecting waste pickers from manual sorting hazards.

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Area of Science:

Environmental Science
Computer Science
Artificial Intelligence

Background:

Efficient solid waste management is critical for urban health amidst rapid industrialization.
Manual waste sorting poses health risks to waste pickers due to a lack of source separation.

Purpose of the Study:

To develop an automated system for municipal solid waste (MSW) classification.
To improve the efficiency and safety of waste management processes.

Main Methods:

Developed MSW-Net, a hierarchical stacking model for automated MSW classification.
Utilized custom Convolutional Neural Network (CNN) and Bayesian-Optimized MobileNet as base models.
Employed Gradient Boosting as the meta-classifier.

Main Results:

MSW-Net achieved 99% training, 95% validation, and 96.43% testing accuracy.
Demonstrated high performance with precision, recall, and F1 scores around 96.42% in testing.
Outperformed existing models in waste sorting accuracy.

Conclusions:

The MSW-Net model offers a highly accurate solution for automated municipal solid waste classification.
This technology can assist municipal authorities in waste management with minimal human intervention.
Enhances waste management efficiency and improves the safety of waste pickers.