Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review

ABSTRACT

Municipal solid waste (MSW) management has become a highly challenging issue for many countries at different levels of development since the growth of population and urbanisation has resulted in a large increase in MSW generation. It is difficult to forecast the solid waste generated and its characteristics due to the non-linear nature of the MSW system, hence the importance of introducing artificial intelligence (AI) techniques and machine learning (ML) methods. This paper discusses a systematic literature review (SLR) on the application of AI techniques in MSW management, including waste generation prediction, waste collection and transportation and waste treatment and final disposal. The study reviewed and analysed the research studies published between 2000 and 2021, and investigated the current challenges faced by researchers in implementing AI approaches in the MSW system. It was concluded that artificial neural networks are the most used approach in various MSW-related problems. However, the lack and reliability of data are limiting the advancement of AI techniques in this field. Additionally, most studies claimed that their results are accurate and can be implemented in real-life scenarios, with an absence of a clear baseline to assess the performance of the adopted approaches. The detailed gaps and future suggestions for AI techniques in MSW systems are also discussed for further research.

GRAPHICAL ABSTRACT

KEYWORDS:

• Waste management
• machine learning
• deep learning
• artificial intelligence
• artificial neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the findings of this study are available within the article.

Additional information

Funding

This work was supported by the Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA) and the CNRST of Morocco, Alkhawarizmi/2020/35.

Notes on contributors

Adnane Mounadel

Adnane Mounadel is a PhD student in Industrial Engineering at ENSEM (National High School of Electricity and Mechanics) in Casablanca, Morocco. He received his Master's degree in Automatic-Signal Processing-Industrial Computing in 2020 from the University of Hassan I Settat, Morocco. His current main research interests concern supply chain management and artificial intelligence.

Hamid Ech-Cheikh

Hamid Echcheikh associate professor at ISEM - Casablanca in Morocco. He got his Ph.D. in industrial engineering, especially in modelling and simulating of supply chain systems. He is currently a researcher at LRI - ENSEM - Casablanca in Morocco, and he is actively involved as a researcher and lecturer in the areas of supply chain management and artificial intelligence.

Saâd Lissane Elhaq

Saad Lissane Elhaq is Professor at ENSEM (high national school of electricity and mechanic) in Casablanca, Morocco. He holds a Ph.D. from the University of Nancy I in France and a Ph.D. in Automation and Industrial Computing from the Mohamed V University of Rabat in Morocco. He is currently a researcher at LRI (Laboratory of Research in engineering). He is actively involved as a researcher and teacher in the fields of production automation and supply chain optimization.

Ahmed Rachid

Ahmed Rachid was born in Casablanca, Morocco, on December 1st, 1960. He received his engineering degree in Cybernetics from ESSTIN (Nancy, France) in 1983, MBA in 1985, PhD in electrical engineering in 1986 and the Habilitation degree in 1991 from the University of Nancy. He has been teaching since 1984 in different universities (Nancy, Lyon) and engineering schools (ESSTIN, Ecole Centrale de Lyon, ESIEE) mainly in the fields of control engineering and signal processing. Since 1992, he is professor at the University of Picardie Jules Verne (France) where he has created its first laboratory in Automatic Systems. His current research includes processes and systems modeling, simulation, control, diagnosis and observation. He has supervised 23 PhD theses and has co-authored 2 patents, 4 books and over 10 scientific papers. He has organized and chaired several international conferences and has coordinated various European research projects and industrial R&D contracts

Mohamed Sadik

Mohamed Sadik received his PhD degree in Electrical Engineering from the National Polytechnic Institue of Lorraine (INPL) (Nancy, France) in 1992. He is currently a full Professor at the Department of Electrical Engineering at the National School of Electrical and Mechanical Engineering (ENSEM). He was chair of the Electrical Engineering Department (2008 - 2013) and was chair of the Research and cooperation department at ENSEM from 2013 to 2019. He has founded and led the Networks, Embedded Systems and Telecom (NEST Reasearch Group component if LRI lab.) (2011 - 2020). Now he is Director of the Laboratory of Research in Engineering (LRI Lab.). His previous research activities are part of the development of autonomous biomedical systems. He is interested in codesign, modeling and synthesis of Embedded Systems, autonomous/intelligent systems. His current research interests include protocols design, ad hoc networking, networking game-theory, pricing and networking neutrality. He interests also to Image processing, machine learning, deep learning, IoT architecture and their applications in 5G/B 6G, smart farming, smart health, smart environment. In the last years, he has some research works in cloud network and cloud Security. He published several peer-reviewed papers in reputed international journals and conferences, some research books and several book chapters. He served on the TPC of many international conferences and as a reviewer of several journals. Pr. Sadik has co-founded the International Symposium on Ubiquitous Networking (UNet) and serves as TPC chair and general Chair of some conferences.

Bilal Abdellaoui

Bilal Abdellaoui is a PhD student in the department of Engineering Research of the National Higher School of Electricity and Mechanics (Morocco). He graduated in Industrial Engineering from the Higher School of Textile and Clothing Industries (Morocco). His research interests cover Supply chain and Industry 4.0.