期刊
WASTE MANAGEMENT
卷 109, 期 -, 页码 231-246出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2020.04.057
关键词
Artificial intelligence; Machine learning; Modeling; Optimization; Deep learning; Neural networks
资金
- University of Sharjah
- OpenUAE Research and Development Group
The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed. (C) 2020 Elsevier Ltd. All rights reserved.
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