Smart City Gnosys
Smart city article details
| Title | Waste Solid Management Using Machine Learning Approch |
|---|---|
| ID_Doc | 61461 |
| Authors | Lahcen G.; Mohamed E.; Mohammed G.; Hanaa H.; Abdelmoula A. |
| Year | 2022 |
| Published | 8th International Conference on Optimization and Applications, ICOA 2022 - Proceedings |
| DOI | http://dx.doi.org/10.1109/ICOA55659.2022.9934356 |
| Abstract | Information and communication technologies (ICT) allow the creation of smart cities to provide better quality services to citizens by exchanging information with the general public. In Morocco, the waste management is the primary challenge for the competent authority to reduce the amount of solid waste generated and satisfy the environmental regulations. The waste collection and treatment plan is the first pillar to optimize in order to better manage the quantities of waste produced by different industrial activities. Smart technologies were identified as alternative solution having the required qualifications for the creation the smart cities. They haves great potential to increase the efficiency and quality of waste collection. High costs and low efficiency are the two main challenges of smart garbage collection. An inconsequent management leads to resources waste at all levels. For example, the city resources are misused and a colossal amount of gasoline is wasted every day. This problem can be solved by managing and protecting all storage spaces using machine learning technics. A key goal of machine learning is the development of algorithms to make future predictions. Machine Learning Based Automatic Waste Recycling Framework has been proposed to classify and separate materials in a mixed recycling application to improve the separation of complex waste. The main purpose of the present paper is to assess machine learning algorithms used in recycling systems. As result, Machine Learning (ML) and Internet of Things (IoT) were proposed for smart waste management to surround the waste collection issue in the smart city. Powered devices can be installed in waste containers, including recycling bins, and provide real-time data on waste-generation. © 2022 IEEE. |
| Author Keywords | Internet of Things; machine learning; smart city; waste management |
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