Research Area: Machine Learning
A variety of problems related to waste management systems can be found in the literature, as they have become tougher to solve over the years. With this in mind, a report of the most influential research concerns in this field could help develop innovative works for solving waste management applications. Literature reviews appear in most introductions and discussion sections of research reports, case reports, and expert opinion papers. It was immediately observed that Cluster Analysis, a multivariate data mining technique, has been used in various applications for sustainability issues. For this reason, this paper shows the results of a Systematic Literature Review on Cluster Analysis techniques applied to waste management. This papers primary goal is to detect what is happening with the applications and techniques in clustering techniques for waste management and, in this way, define possible gaps in this research field. The 61 analyzed papers were categorized into nine application types within the field of waste management (logistics/business; landfill research; theoretical/consequential; waste collection problems; location/selection; monitoring/decision support systems; leachate/water contamination; waste incineration/energy production and, waste forecast/waste production behavior). Following an analysis of their content, gaps were found related to exploring the complex situations in each problem. Instead of using general rules and constraints for their methodologies to solve real-world problems, they resorted to theoretical orientation solutions. Furthermore, suggestions from specialists in the field and more fitting constraints related to the data evaluated could make the works seem less theoretical and more visually applicable.
Keywords:
Waste management
Systematic literature review
Waste collection
Landfill location
Author(s) Name: Fernanda Medeiros Assef , Maria Teresinha Arns Steiner, Edson Pinheiro de Lima
Journal name: Heliyon
Conferrence name:
Publisher name: Cell Press
DOI: 10.1016/j.heliyon.2022.e08784
Volume Information:
