Artificial Intelligence-based applications in municipal waste management can increase efficiency and sustainability.
Authors
Asmae El jaouhari, Laboratory of Technologies and Industrial Services, Sidi Mohamed Ben Abdellah University, Higher School of Technology, Fez, Morocco
Ashutosh Samadhiya, Assistant Professor, Jindal Global Business School, OP Jindal Global University, Sonipat, Haryana, India
Anil Kumar, Guildhall School of Business and Law, London Metropolitan University, London, N7 8DB, UK; Department of Management Studies, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India
Eyob Mulat-weldemeskel, Guildhall School of Business and Law, London Metropolitan University, London, UK
Sunil Luthra, ATAL Cell, All India Council for Technical Education (AICTE), Delhi, India
Rajesh Kumar, Amity School of Business, Amity University, Patna, India
Summary
Rapid urbanization, economic expansion, and population growth have increased waste generation in many nations worldwide. Research on municipal waste management (MWM) is moving towards new frontiers in efficiency and applicability due to the growing amount of data being collected in these systems and the convergence of various technological applications; artificial intelligence (AI) techniques present novel and creative alternatives for MWM.
Even though much research has been conducted in this field, relatively few review studies assess how advancements in AI techniques can contribute to the sustainable advancement of MWM systems. Furthermore, there are discrepancies and a dearth of knowledge regarding the operation of AI-based techniques in MWM. To close this gap, this study conducts a thorough review of the relevant literature with an application of preferred reporting items for systematic reviews and meta-analyses-based methods, examining 229 peer-reviewed publications to explore the role of AI in different MWM areas, such as waste characteristics forecasting, waste bin level monitoring, process parameter prediction, vehicle routing, and MWM planning.
The main AI techniques and models used in MWM optimization, as well as the application areas and stated performance metrics, are all thoroughly analyzed in this review. A conceptual framework is proposed to guide research and practice to take a holistic approach to MWM, along with areas of future study that need to be explored. Researchers, policymakers, municipalities, governments, and other waste management organizations will benefit from this study to minimize costs, maximize efficiency, eliminate the need for manual labor, and change the approach to MWM.
Published in: Journal of Environmental Management
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