[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117264-en":3,"doc-seo-117264-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117264,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Integrating machine learning for the sustainable development of smart cities","Study evaluates how machine learning can advance the Sustainable Development Goals, with emphasis on SDG 11 on sustainable cities and communities. Smart cities rely on ICT and IoT to collect and analyze data for better resource management and reduced environmental impact, while AI improves citizens’ quality of life. The paper surveys ML applications in energy management, transportation efficiency, waste management, and public safety. It also examines real-world initiatives and addresses implementation challenges such as data quality, model interpretability, scalability, and ethical considerations.","TYPE Review  \nPUBLISHED 19 December 2024 DOI 10. 3389/frsc.2024.1449404  \nOPEN ACCESS  \nEDITED BY  \nNitin Goyal,  \nCentral University of Haryana, India  \nREVIEWED BY  \nAntonio Puliafito, University of Messina, Italy Mehdi Gheisari,  \nIslamic Azad University, Iran  \n*CORRESPONDENCE  \nManel Mrabet  \n [M.benrashed@psau.edu.sa](M.benrashed@psau.edu.sa)  \nRECEIVED 15 June 2024  \nACCEPTED 25 November 2024  \nPUBLISHED 19 December 2024  \nCITATION  \nMrabet M and Sliti M (2024) Integrating machine learning for the sustainable development of smart cities.  \nFront. Sustain. Cities 6:1449404 .  \ndoi: 10.3389/frsc.2024.1449404  \nCOPYRIGHT  \n© 2024 Mrabet and Sliti. This is an  \nopen-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIntegrating machine learning for the sustainable development of smart cities  \nManel Mrabet1* and Maha Sliti2  \n1 Department of Computer Sciences, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia, 2 Higher School of Communication of Tunis (SUP’COM), LR11TIC04, Communication Networks and Security Research Lab, LR11TIC02, Green and Smart Communication Systems Research Lab, University of Carthage, Tunis, Tunisia  \nThe 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 e􀀈ciency, waste management, and public safety. It highlights the role of machine learning algorithms to improve operational e􀀈ciency, minimize expenses, and reduce environmental impact. The practical use of ML in smart cities across several countries demonstrates its ability to handle urban challengesand 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.  \nKEYWORDS  \nsustainable smart cities, machine learning, SDG 11, applications, challenges  \n1 Introduction  \nNowadays, urbanization is accelerating, and cities are under increasing pressure to maintain infrastructure, manage resources, and improve the quality of their citizens (Klein and Anderegg, 2021) . The rapid growth of cities creates a number of challenges, including air pollution, tra􀀔c congestion, waste management, and energy use. In this context, new solutions must be implemented, including cutting-edge technologies, to create more e􀀔cient, sustainable, and livable urban environments (Caragliu et al., 2011) . In this context, smart","cbCaifh44XghYxiR","https://ap.wps.com/l/cbCaifh44XghYxiR","pdf",8363242,1,25,"English","en",105,"# Introduction\n## SDG 11 and smart city transformation\n## Role of machine learning in sustainable urban development\n# Applications and practical initiatives\n## Energy management and efficiency\n## Transportation and public safety\n## Waste management and environmental impact\n# Implementation challenges\n## Data quality and accessibility\n## Model interpretability, scalability, and ethics","[{\"question\":\"What is the main purpose of this study on machine learning and smart cities?\",\"answer\":\"To assess how machine learning can support the Sustainable Development Goals, particularly SDG 11, by enabling more sustainable smart city development.\"},{\"question\":\"Which smart city areas are highlighted for machine learning applications?\",\"answer\":\"Energy management, transportation efficiency, waste management, and public safety are discussed as key application areas.\"},{\"question\":\"What challenges are identified when implementing machine learning in smart city projects?\",\"answer\":\"Challenges include data quality and accessibility, model interpretability, scalability, and ethical considerations, along with the need to balance analysis needs with privacy and 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