Artificial intelligence applications in solid waste management: A systematic research review

M Abdallah, MA Talib, S Feroz, Q Nasir, H Abdalla… - Waste Management, 2020 - Elsevier
The waste management processes typically involve numerous technical, climatic,
environmental, demographic, socio-economic, and legislative parameters. Such complex …

Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches

K Lin, Y Zhao, JH Kuo, H Deng, F Cui, Z Zhang… - Journal of Cleaner …, 2022 - Elsevier
Increasing generation of municipal solid waste, heterogeneity of waste composition, and
complex processes of waste management and recovery have limited the performance of …

Recent advances in applications of artificial intelligence in solid waste management: A review

I Ihsanullah, G Alam, A Jamal, F Shaik - Chemosphere, 2022 - Elsevier
Efficient management of solid waste is essential to lessen its potential health and
environmental impacts. However, the current solid waste management practices encounter …

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

W Xia, Y Jiang, X Chen, R Zhao - Waste Management & …, 2022 - journals.sagepub.com
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 …

Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review

A Xu, H Chang, Y Xu, R Li, X Li, Y Zhao - Waste Management, 2021 - Elsevier
Artificial neural networks (ANNs) have recently attracted significant attention in
environmental areas because of their great self-learning capability and good accuracy in …

Estimating construction waste generation in the Greater Bay Area, China using machine learning

W Lu, J Lou, C Webster, F Xue, Z Bao, B Chi - Waste management, 2021 - Elsevier
Reliable construction waste generation data is a prerequisite for any evidence-based waste
management effort, but such data remains scarce in many developing economies owing to …

Development of machine learning-based models to forecast solid waste generation in residential areas: A case study from Vietnam

XC Nguyen, TTH Nguyen, DD La, G Kumar… - Resources …, 2021 - Elsevier
The main aim of this work was to compare six machine learning (ML)-based models to
predict the municipal solid waste (MSW) generation from selected residential areas of …

Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe–results from the GAINS model

L Höglund-Isaksson, A Gómez-Sanabria… - Environmental …, 2020 - iopscience.iop.org
Methane is the second most important greenhouse gas after carbon dioxide contributing to
human-made global warming. Keeping to the Paris Agreement of staying well below two …

[HTML][HTML] A decision support system for classifying supplier selection criteria using machine learning and random forest approach

MR Ali, SMA Nipu, SA Khan - Decision Analytics Journal, 2023 - Elsevier
Supplier selection is an important process in supply chain management that sets a
foundation for a long-term partnership with suppliers that can greatly contribute to the …

Locating a disinfection facility for hazardous healthcare waste in the COVID-19 era: a novel approach based on Fermatean fuzzy ITARA-MARCOS and random forest …

V Simic, A Ebadi Torkayesh… - Annals of operations …, 2023 - Springer
Hazardous healthcare waste (HCW) management system is one of the most critical urban
systems affected by the COVID-19 pandemic due to the increase in waste generation rate in …