Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions

Abstract

Effective municipal solid waste management is essential for public health, environmental protection, economic benefits, and clean energy generation for future commercial applications. However, challenges like real-time monitoring, automated sorting systems, optimized collection routes, predictive maintenance, and public education and engagement hinder efficiency. Machine learning can address these challenges through real-time monitoring, automated sorting, route optimization, predictive maintenance, and targeted public education. Supervised, unsupervised, and reinforcement learning can be applied to various waste management processes, enhancing energy extraction and clean fuel production for commercial sectors like the steel industry. Machine learning can effectively predict waste generation, design collection routes, classify waste materials, forecast real-time landfill filling rates, detect operational issues, prevent illicit dumping, and establish predictive maintenance systems. However, it must be integrated with other strategies, policies, and regulations for a sustainable waste management system. Additionally, cost-benefit analyses, scalability, and implementation feasibility should be considered before investing. In conclusion, machine learning can improve municipal solid waste management efficiency and effectiveness, but further research is needed. The present study offers vital knowledge for key stakeholders, including successful case studies and evaluations of societal technology and customer readiness levels.