Solid Waste Generation and Disposal Using Machine Learning Approaches: A Survey of Solutions and Challenges

Abdallah Namoun (), Ali Tufail, Muhammad Yasar Khan, Ahmed Alrehaili, Toqeer Ali Syed and Oussama BenRhouma
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Abdallah Namoun: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia
Ali Tufail: School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong BE1410, Brunei
Muhammad Yasar Khan: Malaysian Institute of Information Technology (MIIT), University of Kuala Lumpur, Kuala Lumpur 50250, Malaysia
Ahmed Alrehaili: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia
Toqeer Ali Syed: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia
Oussama BenRhouma: Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia

Sustainability, 2022, vol. 14, issue 20, 1-32

Abstract: We present a survey of machine learning works that attempt to organize the process flow of waste management in smart cities. Unlike past reviews, we focused on the waste generation and disposal phases in which citizens, households, and municipalities try to eliminate their solid waste by applying intelligent computational models. To this end, we synthesized and reviewed 42 articles published between 2010 and 2021. We retrieved the selected studies from six major academic research databases. Next, we deployed a comprehensive data extraction strategy focusing on the objectives of studies, trends of ML adoption, waste datasets, dependent and independent variables, and AI-ML-DL predictive models of waste generation. Our analysis revealed that most studies estimated waste material classification, amount of generated waste per area, and waste filling levels per location. Demographic data and images of waste type and fill levels are used as features to train the predictive models. Although various studies have widely deployed artificial neural networks (ANN) and convolutional neural networks (CNN) to classify waste, other techniques, such as gradient boosting regression tree (GBRT), have also been utilized. Critical challenges hindering the prediction of solid waste generation and disposal include the scarcity of real-time time series waste datasets, the lack of performance benchmarking tests of the proposed models, the reliability of the analytics models, and the long-term forecasting of waste generation. Our survey concludes with the implications and limitations of the selected models to inspire further research efforts.

Keywords: smart waste management; solid waste generation; waste disposal; artificial intelligence; machine learning; deep learning; smart cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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