TY - JOUR
T1 - Solid waste management techniques powered by in-silico approaches with a special focus on municipal solid waste management
T2 - Research trends and challenges
AU - Vyas, Shaili
AU - Dhakar, Kusum
AU - Varjani, Sunita
AU - Singhania, Reeta Rani
AU - Bhargava, Preeti Chaturvedi
AU - Sindhu, Raveendran
AU - Binod, Parameswaran
AU - Wong, Jonathan W.C.
AU - Bui, Xuan Thanh
N1 - Funding Information:
Shaili Vyas is grateful to the authorities of the Swinburne University of Technology for encouragement and support.
Publisher Copyright:
© 2023 Elsevier B.V. All rights reserved.
PY - 2023/9/15
Y1 - 2023/9/15
N2 - Many technical, climatic, environmental, biological, financial, educational, and regulatory factors are typically involved in solid waste management (SWM). Artificial Intelligence (AI) techniques have lately gained attraction in providing alternative computational methods for resolving problems of solid waste management. The purpose of this review is to direct solid waste management researchers taking an interest in the use of artificial intelligence in their area of study through main research elements such as AI models, their own benefits and drawbacks, effectiveness, and applications. The major AI technologies recognized are discussed in the subsections of the review, which contains a specific fusion of AI models. It also covers research that equated AI technologies to other non-AI methodologies. The section that follows contains a brief debate of the numerous SWM disciplines where AI was consciously applied. The article concludes with progress, challenges and perspectives in implementing AI-based solid waste management.
AB - Many technical, climatic, environmental, biological, financial, educational, and regulatory factors are typically involved in solid waste management (SWM). Artificial Intelligence (AI) techniques have lately gained attraction in providing alternative computational methods for resolving problems of solid waste management. The purpose of this review is to direct solid waste management researchers taking an interest in the use of artificial intelligence in their area of study through main research elements such as AI models, their own benefits and drawbacks, effectiveness, and applications. The major AI technologies recognized are discussed in the subsections of the review, which contains a specific fusion of AI models. It also covers research that equated AI technologies to other non-AI methodologies. The section that follows contains a brief debate of the numerous SWM disciplines where AI was consciously applied. The article concludes with progress, challenges and perspectives in implementing AI-based solid waste management.
KW - Artificial intelligence
KW - Artificial neural networks
KW - Genetic algorithm
KW - Municipal solid waste
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85161695196&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.164344
DO - 10.1016/j.scitotenv.2023.164344
M3 - Journal article
C2 - 37244611
AN - SCOPUS:85161695196
SN - 0048-9697
VL - 891
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 164344
ER -
