Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries.
|Number of pages||21|
|Early online date||25 Jan 2021|
|Publication status||Published - Jun 2021|
The final publication is available at Springer via http://dx.doi.org/10.1007/s00500-021-05579-7
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This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
FunderNational Natural Science Foundation of China (Grant Nos. 71671019, 72071021, and 71871034).
- Literature review
- Machine learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Geometry and Topology