基于人工智能的智慧城市特征描述

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC Smart Cities Pub Date : 2024-06-07 DOI:10.3390/smartcities7030056
L. Hammoumi, M. Maanan, H. Rhinane
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引用次数: 1

摘要

全世界的城市都在试图被贴上智慧城市的标签,但真正将城市划分为智慧城市仍是一个巨大的挑战。本研究旨在利用人工智能(AI)对智慧城市的表现进行分类,并找出与智慧城市相关的因素。根据居民对城市结构和技术应用的看法,这项研究涵盖了全球 200 个城市。在 147 个城市中,我们通过 39 个问题的调查收集了每个城市 120 名居民的看法,这些问题涉及两个主要支柱:"结构",指城市现有的基础设施;"技术 "支柱,描述居民可用的技术供应和服务。这些支柱的评估涉及五个关键领域:健康与安全、流动性、活动、机遇和治理。其余 53 个城市的得分是通过分析从各种在线资源中收集到的相关数据得出的。为了选出最佳算法,对随机森林、人工神经网络、支持向量机和梯度提升等多种机器学习算法进行了测试和比较。结果表明,随机森林和人工神经网络是经过训练的最佳模型,准确率最高。这项研究为使用机器学习识别和评估智慧城市提供了一个强大的框架,为未来的研究和城市规划提供了宝贵的见解。
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Characterizing Smart Cities Based on Artificial Intelligence
Cities worldwide are attempting to be labelled as smart, but truly classifying as such remains a great challenge. This study aims to use artificial intelligence (AI) to classify the performance of smart cities and identify the factors linked to their smartness. Based on residents’ perceptions of urban structures and technological applications, this study included 200 cities globally. For 147 cities, we gathered the perceptions of 120 residents per city through a survey of 39 questions covering two main pillars: ‘Structures’, referring to the existing infrastructure of the city, and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. These pillars were evaluated across five key areas: health and safety, mobility, activities, opportunities, and governance. For the remaining 53 cities, scores were derived by analyzing pertinent data collected from various online resources. Multiple machine learning algorithms, including Random Forest, Artificial Neural Network, Support Vector Machine, and Gradient Boost, were tested and compared in order to select the best one. The results showed that Random Forest and the Artificial Neural Network are the best trained models that achieved the highest levels of accuracy. This study provides a robust framework for using machine learning to identify and assess smart cities, offering valuable insights for future research and urban planning.
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来源期刊
Smart Cities
Smart Cities Multiple-
CiteScore
11.20
自引率
6.20%
发文量
0
审稿时长
11 weeks
期刊介绍: Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.
期刊最新文献
Energy Management System for a Residential Positive Energy District Based on Fuzzy Logic Approach (RESTORATIVE) Human-Centric Collaboration and Industry 5.0 Framework in Smart Cities and Communities: Fostering Sustainable Development Goals 3, 4, 9, and 11 in Society 5.0 Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities Data-Driven Reliability Prediction for District Heating Networks
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