Application of machine learning algorithms in municipal solid waste management: A mini review

Area of Science:

• Environmental Science
• Computer Science
• Engineering

Background:

• Rapid urbanization and population growth increase municipal solid waste (MSWM).
• Advanced technologies, particularly machine learning (ML), are increasingly vital for sustainable MSWM.
• ML algorithms excel at modeling complex, nonlinear environmental processes.

Purpose of the Study:

• To comprehensively review and analyze the application of ML algorithms in MSWM over the last two decades (2000-2020).
• To summarize ML's role throughout the entire MSWM process, from generation to disposal.
• To identify research gaps and future directions for ML in MSWM.

Main Methods:

• Systematic literature review of over 200 publications from 2000-2020.
• Analysis of ML algorithm applications across all stages of municipal solid waste management.
• Synthesis of findings to identify trends, challenges, and opportunities.

Main Results:

• ML has been applied across the entire MSWM chain, including waste generation prediction, collection optimization, and disposal strategies.
• Various ML algorithms demonstrate significant potential in enhancing the efficiency and sustainability of MSWM.
• The review highlights a growing body of research integrating ML into waste management practices.

Conclusions:

• ML offers powerful tools for addressing the challenges of increasing municipal solid waste.
• Further research is needed to explore novel ML applications and address existing gaps in MSWM.
• This review provides guidance for future theoretical and practical advancements in ML-driven MSWM.