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

• Published in: Waste management & research Vol. 40; no. 6; pp. 609 - 624
• Main Authors: Xia, Wanjun, Jiang, Yanping, Chen, Xiaohong, Zhao, Rui
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.06.2022; Sage Publications Ltd
• Subjects: Algorithms; Cities; Garbage collection; Learning algorithms; Machine Learning; Mini-Review; Municipal solid waste; Municipal waste management; Population growth; Refuse Disposal; Solid Waste; Solid waste management; Sustainable development; Urbanization; Waste Management; Cities; sustainable development; deep learning; machine learning; data-driven; Municipal solid waste management
• ISSN: 0734-242X; 1096-3669; 1096-3669
• DOI: 10.1177/0734242X211033716

Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000–2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.