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

Szczegóły
Abstrakt

Tytuł:: Application of machine learning algorithms in municipal solid waste management: A mini review.
Autorzy:: Xia W; School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China.; Library, Southwest Jiaotong University, Chengdu, Sichuan, China.
Jiang Y; Library, Southwest Jiaotong University, Chengdu, Sichuan, China.
Chen X; Library, Southwest Jiaotong University, Chengdu, Sichuan, China.
Zhao R; Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China.
Źródło:: Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA [Waste Manag Res] 2022 Jun; Vol. 40 (6), pp. 609-624. Date of Electronic Publication: 2021 Jul 16.
Typ publikacji:: Journal Article; Review
Język:: English
Imprint Name(s):: Publication: : London : Sage Pulbications
Original Publication: London ; New York : Academic Press, c1983-
MeSH Terms:: Refuse Disposal*
Waste Management*
Algorithms ; Cities ; Machine Learning ; Solid Waste
References:: Waste Manag. 2020 Jul 15;113:508-520. (PMID: 32546447)
Chemosphere. 2013 Jul;92(3):258-64. (PMID: 23562548)
Environ Monit Assess. 2014 Sep;186(9):5381-91. (PMID: 24829160)
Sci Total Environ. 2020 Jan 10;699:134279. (PMID: 33736193)
Waste Manag. 2015 Jan;35:29-35. (PMID: 25453316)
Waste Manag. 2011 Mar;31(3):595-602. (PMID: 20933381)
Waste Manag Res. 2020 Aug;38(8):840-850. (PMID: 32122291)
Nat Commun. 2020 Sep 25;11(1):4880. (PMID: 32978375)
Waste Manag. 2018 Apr;74:3-15. (PMID: 29221873)
Waste Manag. 2020 Sep;115:8-14. (PMID: 32707482)
Comput Intell Neurosci. 2018 Nov 1;2018:5060857. (PMID: 30515197)
Waste Manag. 2016 Sep;55:3-11. (PMID: 26522806)
Waste Manag. 2016 Feb;48:14-23. (PMID: 26482809)
Environ Sci Pollut Res Int. 2019 Feb;26(4):3368-3381. (PMID: 30511225)
J Air Waste Manag Assoc. 2014 Feb;64(2):150-9. (PMID: 24654384)
Waste Manag. 2020 May 15;109:1-9. (PMID: 32361385)
Waste Manag. 2019 Apr 1;88:118-130. (PMID: 31079624)
Environ Monit Assess. 2013 Nov;185(11):9435-49. (PMID: 23729162)
J Environ Manage. 2012 Aug 15;104:9-18. (PMID: 22484654)
Waste Manag. 2020 Apr 15;107:182-190. (PMID: 32299033)
Science. 2018 Jul 27;361(6400):360-365. (PMID: 30049875)
Waste Manag. 2016 Oct;56:13-22. (PMID: 27297046)
Waste Manag. 2018 Sep;79:781-790. (PMID: 30343811)
Waste Manag. 2017 Apr;62:3-11. (PMID: 28216080)
Environ Monit Assess. 2018 Sep 20;190(10):597. (PMID: 30238169)
J Environ Health Sci Eng. 2020 Jun 19;18(2):687-697. (PMID: 33312594)
Waste Manag. 2020 Oct;116:66-78. (PMID: 32784123)
Bioresour Technol. 2019 Oct;290:121761. (PMID: 31302465)
Waste Manag. 2019 Feb 1;84:129-140. (PMID: 30691884)
Heliyon. 2019 Nov 14;5(11):e02810. (PMID: 31763474)
Waste Manag. 2020 Jul 15;113:379-394. (PMID: 32580105)
Sci Total Environ. 2019 Jul 15;674:19-25. (PMID: 31003083)
Waste Manag. 2019 Mar 15;87:782-790. (PMID: 31109582)
Waste Manag. 2017 Oct;68:186-197. (PMID: 28408281)
Int J Environ Res Public Health. 2020 Jul 09;17(14):. (PMID: 32660117)
Waste Manag. 2018 Jul;77:477-485. (PMID: 29724480)
Bioresour Technol. 2020 May;303:122926. (PMID: 32035386)
Contributed Indexing:: Keywords: Municipal solid waste management; data-driven; deep learning; machine learning; sustainable development
Substance Nomenclature:: 0 (Solid Waste)
Entry Date(s):: Date Created: 20210716 Date Completed: 20220420 Latest Revision: 20220716
Update Code:: 20240105
PubMed Central ID:: PMC9016669
DOI:: 10.1177/0734242X211033716
PMID:: 34269157
: Czasopismo naukowe

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.

Informacja