Application of big data analytics to forecast future waste trends and inform sustainable planning

Adeleye Yusuff Adewuyi 1, Kayode Blessing Adebayo 2, Damilola Adebayo 3, Joseph Moses Kalinzi 4, Uyiosa Osarumen Ugiagbe 5, Oyindamola Omolara Ogunruku 6, Olufemi Adeleye Samson 7, Onabolujo Richard Oladele 8 and Samson Ademola Adeniyi 9, *

1 The Romain College of Business, University of Southern Indiana, Evansville, USA.
2 Department of Agricultural and Biosystems Engineering, South Dakota State University, USA.
3 Department of Mechanical Engineering, Teesside University, United Kingdom.
4 Department of Computer Science, Southern Illinois University Carbondale, Illinois, USA.
5 Department of Mathematics, Science, and Social Studies Education, University of Georgia, Athens, USA.
6 Accounting Finance Economics and Decisions, Western Illinois University, Illinois, USA.
7 Department of Computing and Mathematical Science, School of Engineering, University of Wolverhampton.
8 Managing Innovation and Information Technology, Salford Business School, University of Salford, Manchester.
9 College of Computing and Informatics, Drexel University, PA. USA.
 
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 2469–2479
Article DOI: 10.30574/wjarr.2024.23.1.2229
Publication history: 
Received on 15 June 2024; revised on 21 July 2024; accepted on 24 July 2024
 
Abstract: 
Urbanization and industrialization have caused a substantial rise in garbage production, creating substantial environmental, economic, and social difficulties. Precise prediction of future waste patterns is essential for sustainable waste management and strategic planning. Big Data Analytics (BDA) presents a potential method for examining large quantities of waste-related data, revealing trends, and offering practical insights. This review article examines the utilization of Big Data Analytics (BDA) in predicting future waste patterns, emphasizing its capacity to provide valuable insights for sustainable planning and decision-making. The study examines several techniques, tools, and case studies, and explores the obstacles and future prospects in this new discipline.
 
Keywords: 
Big Data; Sustainable; Data Analytics; Waste Management; Machine Learning
 
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Copyright information: 
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

 

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