Smart City Gnosys
Smart city article details
| Title | Integrating Machine Learning For The Sustainable Development Of Smart Cities |
|---|---|
| ID_Doc | 32035 |
| Authors | Mrabet M.; Sliti M. |
| Year | 2024 |
| Published | Frontiers in Sustainable Cities, 6 |
| DOI | http://dx.doi.org/10.3389/frsc.2024.1449404 |
| Abstract | The purpose of this study is to assess the potential of machine learning in advancing the Sustainable Development Goals, particularly Goal 11, which focuses on sustainable urban and community development. To reduce the impacts of increasing urbanization on the environment, it is necessary to prioritize the sustainable development of smart cities. Smart cities use information and communication technology techniques to enhance sustainability by improving resource management and reducing environmental impact. In this context, the use of artificial intelligence enhances the overall quality of life, which is a critical component of sustainable smart cities. Machine learning, a subset of artificial intelligence, is crucial in promoting the development of sustainable smart cities. This study focuses on the application of machine learning in sustainable smart cities, ranging from energy management, transportation efficiency, waste management, and public safety. It highlights the role of machine learning algorithms to improve operational efficiency, minimize expenses, and reduce environmental impact. The practical use of ML in smart cities across several countries demonstrates its ability to handle urban challenges and increase sustainability. This paper discusses a variety of real-world initiatives that have successfully employed machine learning to develop sustainable smart cities, as well as in-depth studies of the ML algorithms used and the obtained results. The paper also covers the challenges of implementing machine learning into smart city projects, such as data quality, model interpretability, scalability, and ethical considerations. It emphasizes the importance of high-quality data, clear models, and the right use of machine learning tools. |
| Author Keywords | applications; challenges; machine learning; SDG 11; sustainable smart cities |
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