ReviewWaste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA2022
Application of machine learning algorithms in municipal solid waste management: A mini review.
Review in Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of AI-Based Techniques for Automated Waste Classification.Sensors (Basel, Switzerland) · 2025Pooled it
- Integrating automation into construction waste quantification: A review of digital innovations for sustainable resource management.Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2026Review
- Advanced predictive modeling of municipal solid waste management using robust machine learning.Scientific reports · 2025Article
- Machine learning-based prediction of heating values in municipal solid waste.Scientific reports · 2025Article
- Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data.Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2025Article
- Wireless sensor network-based machine learning framework for smart cities in intelligent waste management.Heliyon · 2024Article
- Review
- Artificial intelligence for waste management in smart cities: a review.Environmental chemistry letters · 2023Review
- Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities.International journal of environmental research and public health · 2023Article
- Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas.International journal of environmental research and public health · 2022Article
- Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities.Multimedia tools and applications · 2022Article
- An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.