Exploring Intelligent Technologies for Waste Management: A Comprehensive Literature Review

The fast growth of information and communication technology (ICT) has made it a necessary component of contemporary solid waste management (SWM) system planning and design. ICTs are divided into four sections to assist in the planning, monitoring, collection, and management of solid waste: spatial technologies, identification technologies, data collecting technologies, and data transmission technologies. The basis of the ICT-based SWM systems categorized in this article is formed by the first three technologies, with the fourth technology being utilized by almost all systems. Waste, in all its forms solid, gaseous, and liquid increases and affects the entire world as a result of population expansion, urbanization, and industry. Waste management includes reuse, recycling, reducing the amount of rubbish produced, and other strategies to mitigate the effects of waste generation brought on by increasing population growth and industrialization. In order to solve issues with trash creation, waste collection, waste transportation, waste treatment, and waste disposal methods, monitoring is one of the most crucial parts of waste management. In waste management, artificial intelligence (AI) solutions were proposed, such as the application of convolutional neural networks and other AI technologies for efficient garbage recognition and sorting. Spatial analysis is a prominent area of study in environmental studies and collection, archiving, analysis, and mapping of spatial data. Topology, raster, attribute data, and features make up the majority of spatial data.

Authors

Institutions

Publication Details

Journal
Journal of Scientific Research and Reports
Published
2024-12-29
DOI
https://doi.org/10.9734/jsrr/2024/v30i122726
Primary Topic
Organizational and Employee Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Intelligent Technologies for Waste Management: A Comprehensive Literature Review

Chitrasena Padhy, Akhila Badavath, Ganamala Rathna, Neha Pragnya
Journal of Scientific Research and Reports
Organizational and Employee Performance
article

Exploring Intelligent Technologies for Waste Management: A Comprehensive Literature Review

Chitrasena Padhy, Akhila Badavath, Ganamala Rathna, Neha Pragnya
article en

Abstract

The fast growth of information and communication technology (ICT) has made it a necessary component of contemporary solid waste management (SWM) system planning and design. ICTs are divided into four sections to assist in the planning, monitoring, collection, and management of solid waste: spatial technologies, identification technologies, data collecting technologies, and data transmission technologies. The basis of the ICT-based SWM systems categorized in this article is formed by the first three technologies, with the fourth technology being utilized by almost all systems. Waste, in all its forms solid, gaseous, and liquid increases and affects the entire world as a result of population expansion, urbanization, and industry. Waste management includes reuse, recycling, reducing the amount of rubbish produced, and other strategies to mitigate the effects of waste generation brought on by increasing population growth and industrialization. In order to solve issues with trash creation, waste collection, waste transportation, waste treatment, and waste disposal methods, monitoring is one of the most crucial parts of waste management. In waste management, artificial intelligence (AI) solutions were proposed, such as the application of convolutional neural networks and other AI technologies for efficient garbage recognition and sorting. Spatial analysis is a prominent area of study in environmental studies and collection, archiving, analysis, and mapping of spatial data. Topology, raster, attribute data, and features make up the majority of spatial data.

Journal of Scientific Research and ReportsVol. 30(12)
Institute of Plant Genetics, Polish Academy of Sciences (PL), Centurion University of Technology and Management (IN)
Sustainable cities and communities
Openalex Percentile: Top 77%
Organizational and Employee Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.