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This study uses artificial intelligence to classify smart city performance and the related factors based on residents’ perceptions. Data from 200 cities are used, with 147 cities surveyed via 39 questions across two pillars (Structures and Technology) and five evaluation areas. The remaining 53 cities are scored using openly available internet data. Random Forest and Artificial Neural Network show the highest accuracy.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-ai-for-identifying-smart-cities-research-overview/290457/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-ai-for-identifying-smart-cities-research-overview/290457.png","ImageObject",300,407,{"name":92,"@type":93},"Hazel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study classify whether a city is smart?","Question",{"text":112,"@type":113},"It uses artificial intelligence to classify smart city performance and the linked factors. Classification is based on residents’ perceptions for most cities and on openly available online data for the rest.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data sources and survey structure are used for the 147 cities?",{"text":117,"@type":113},"The study collects perceptions from 120 residents per city by answering a survey of 39 questions. The questions are grouped into two pillars—Structures (infrastructure) and Technology (technological provisions and services)—and each pillar is evaluated across five areas: health and safety, mobility, activities, opportunities, and governance.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning algorithms perform best in the experiments?",{"text":121,"@type":113},"Random Forest (RF) together with Artificial Neural Network (ANN) achieves the highest accuracy and are selected as the best trained models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290457,1789641731,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":46,"preview_page_count":4},137441390410,"https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984","MACHINE LEARNING (AI) FOR IDENTIFYING SMART CITIES  \nL. Hammoumi 1*, H. Rhinane 1  \n1 Geoscience Laboratory, Aïn Chock Faculty of Science, Km 8 El Jadida Road, BP:5366,  \nCasablanca, [Morocco-laaziza.hammoumi@gmail.com](Morocco-laaziza.hammoumi@gmail.com)  \nKEY WORDS: Smart city indicators, Cities, Machine Learning, Artificial intelligence, Classification.  \nABSTRACT:  \nCities worldwide are attempting to be claimed as smart, but truly classifying as such remains a great challenge. This paper aims to use artificial intelligence AI to classify the smart city's performance as well as the factors linked to it. This is based on the perceptions of residents on issues related to structures and technology applications available in their cities. To achieve this goal, the study included 200 cities worldwide. For 147 cities we captured the perceptions of 120 residents in each city, by answering a survey of 39 questions evolving around two main Pillars: ‘Structures’ that refers to the existing infrastructure of the city and the‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. And each one is evaluated under five key areas: health and safety, mobility, activities, opportunities, and governance. The final score of the other 53 cities, was measured by using the data openly available on the internet. And this by means of different algorithms of machine learning such as Random Forest RF, Artificial Neural Network ANN, Support Vector Machine (SVM), and Gradient Boost (XGB) . These algorithms have been compared and evaluated in order to select the best one. The tests showed that Random Forest RF alongside with Artificial Neural Network ANN, with the highest level of accuracy, are the best trained model. This study will enable other researches to use machine learning in the identification process of smart cities.  \n1. INTRODUCTION  \nThe concept of smart cities has evolved over time, encompassing different aspects and definitions(Caragliu et al., 2011) . Early discussions laid the foundations for understanding the multidimensional nature of smart cities. Harrison et al.(2010) presented a comprehensive view of smart cities, highlighting the integration of various technologies, data, and systems to enhance urban functions. Caragliu et al. (2011) further emphasized the role of information and communication technologies (ICT) in improving the efficiency and competitiveness of cities. As the concept matured, scholars recognized the importance of sustainability in smart city initiatives (Lombardi et al., 2012; Albino et al., 2015) . The idea of a smart city gradually evolved into a holistic approach that seeks to address environmental, social, and economic challenges through innovative technologies and data-driven solutions (Harrison et al., 2010) .  \nIn the pursuit of identifying smart cities, researchers and practitioners have employed diverse methods and indicators. Sustainability has been a prominent aspect, with a focus on reducing resource consumption, promoting renewable energy, and improving environmental quality (Neirotti et al., 2014; Mora & Bolici, 2017) . The notion of smartness, referring to the integration and utilization of advanced technologies, has also played a pivotal role in distinguishing smart cities (Batty, 2013; Angelidou, 2015) . Moreover, performance indicators have been employed to evaluate the effectiveness and efficiency of smart city initiatives (Lai & Cole, 2022) . These indicators encompass various dimensions such as governance, innovation, quality of life, and economic development (Huovila et al., 2019; Javed et al., 2022) . By relying on such indicators, researchers and policymakers have attempted to quantify and compare the smartness of different cities (Albino et al., 2015) .  \nHowever, these classical methods for identifying smart cities often rely on manual data collection and subjective assessments. This approach poses limitations in terms of scalability, ob","cbCaityVre6r5ULo","https://ap.wps.com/l/cbCaityVre6r5ULo","pdf",1201291,"English","# Introduction\n## Smart city concept evolution\n## Indicators and traditional identification methods\n## Role of machine learning in smart city identification","[{\"question\":\"How does the study classify whether a city is smart?\",\"answer\":\"It uses artificial intelligence to classify smart city performance and the linked factors. Classification is based on residents’ perceptions for most cities and on openly available online data for the rest.\"},{\"question\":\"What data sources and survey structure are used for the 147 cities?\",\"answer\":\"The study collects perceptions from 120 residents per city by answering a survey of 39 questions. The questions are grouped into two pillars—Structures (infrastructure) and Technology (technological provisions and services)—and each pillar is evaluated across five areas: health and safety, mobility, activities, opportunities, and governance.\"},{\"question\":\"Which machine learning algorithms perform best in the experiments?\",\"answer\":\"Random Forest (RF) together with Artificial Neural Network (ANN) achieves the highest accuracy and are selected as the best trained models.\"}]","MACHINE LEARNING (AI) FOR IDENTIFYING SMART CITIES - Research overview | PDF"]