State-of-the-art applications of machine learning in the life cycle of solid waste management

Highlights

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.

● Changes of research field over time, space, and hot topics were analyzed.

● Detailed application seniors of ML on the life cycle of SW were summarized.

● Perspectives towards future development of ML in the field of SW were discussed.

Abstract

Due to the superiority of machine learning (ML) data processing, it is widely used in research of solid waste (SW). This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW. Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress. Based on the articles considered, a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened. The three topics of ML, namely, SW categories, ML algorithms, and specific applications, as applied to the life cycle of SW were discussed. ML has been applied during the entire SW process, thereby affecting its life cycle. ML was used to predict the generation and characteristics of SW, optimize its collection and transportation, and model the processing of its energy utilization. Finally, the current challenges of applying ML to SW and future perspectives were discussed. The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW. ML plays an important role in the modernization and intellectualization of SW management. It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal.

Graphical abstract

Keywords

Machine learning (ML) / Solid waste (SW) / Bibliometrics / SW management / Energy utilization / Life cycle

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Acknowledgement

This research was supported by the National Natural Science Foundation of China (No. 52100157).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1644-x and is accessible for authorized users.

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