Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Solid waste management (SWM) processes are evolving with new technologies for enhanced sustainability.
• Emerging tools and techniques aim to improve the environmental, social, and economic aspects of SWM.

Purpose of the Study:

• To systematically analyze Knowledge Discovery in Databases (KDD) and Data Mining (DM) techniques and tools applied to SWM.
• To explore the technological potential of KDD and DM in optimizing waste collection and transport stages.
• To identify challenges and opportunities of KDD and DM in the context of SWM.

Main Methods:

• Systematic literature review using PICOC and PRISMA protocols.
• Analysis of 62 documents from the Web of Science database.
• Identification and categorization of KDD and DM tools and techniques used in SWM.

Main Results:

• MATLAB (29.7%) and GIS (13.5%) are the most utilized tools.
• Artificial Neural Networks (35.8%), Linear Regression (16.0%), and Support Vector Machine (12.3%) are the predominant techniques.
• A significant portion of studies originate from China (15.3%) and India (11.1%).
• Data collection and treatment pose major challenges, while opportunities lie in sustainable development impacts.

Conclusions:

• KDD and DM offer significant potential for optimizing SWM processes.
• Addressing data challenges is crucial for realizing the full benefits of these technologies.
• The application of KDD and DM in SWM can substantially contribute to sustainable development goals.