Urban resilience and livability performance of European smart cities: A novel machine learning approach

弹性(材料科学) 支持向量机 随机森林 机器学习 人工智能 智慧城市 公制(单位) 朴素贝叶斯分类器 聚类分析 计算机科学 工程类 物联网 计算机安全 运营管理 热力学 物理
作者
Adeeb A. Kutty,Tadesse G. Wakjira,Murat Küçükvar,Galal M. Abdella,Nuri C. Onat
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:378: 134203-134203 被引量:111
标识
DOI:10.1016/j.jclepro.2022.134203
摘要

Smart cities are centres of economic opulence and hope for standardized living. Understanding the shades of urban resilience and livability in smart city models is of paramount importance. This study presents a novel two-stage data-driven framework combining a multivariate metric-distance analysis with machine learning (ML) techniques for resilience and livability assessment of smart cities. A longitudinal dataset for 35 top-ranked European smart cities from 2015 till 2020 applied as the case study under the proposed framework. Initially, a metric distance-based weighting approach is used to weight the indicators and quantify the scores across each aspect under city resilience and urban livability. The key aspects under city resilience include social, economic, infrastructure and built environment and, institutional resilience, while under urban livability, the aspects include accessibility, community well-being, and economic vibrancy. Fuzzy c-means clustering as an unsupervised machine learning technique is used to sort smart cities based on the degree of performance. In addition, an intelligent approach is presented for the prediction of the degree of livability, resilience, and aggregate performance of smart cities based on various supervised ML techniques. Classification models such as Naïve Bayes, k-nearest neighbors (kNN), support vector machine (SVM), Classification and Regression Tree (CART) and, ensemble models including Random Forest (RF) and Gradient Boosting machine (GBM) were used. Three coefficients (accuracy, Cohen's Kappa (κ) and average area under the precision-recall curve (AUC-PR)) along with confusion matrix were used to appraise the performance of the classifier ML models. The results revealed GBM as the best classification and predictive model for the resilience, livability, and aggregate performance assessment. The study also revealed Copenhagen, Geneva, Stockholm, Munich, Helsinki, Vienna, London, Oslo, Zurich, and Amsterdam as the smart cities that co-create resilience and livability in their development model with superior performance.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大力的灵雁应助王博林采纳,获得10
刚刚
去海边吗完成签到,获得积分10
刚刚
科研通AI6.4应助任性烧鹅采纳,获得10
刚刚
1秒前
2秒前
li发布了新的文献求助10
2秒前
LiuShuhao完成签到,获得积分10
3秒前
海纳百川发布了新的文献求助10
4秒前
李霄炫发布了新的文献求助10
5秒前
LFY完成签到,获得积分10
8秒前
zfj完成签到 ,获得积分10
8秒前
9秒前
Raskye完成签到,获得积分10
9秒前
QJH完成签到,获得积分10
10秒前
海纳百川完成签到,获得积分10
10秒前
Yan要高飞发布了新的文献求助10
12秒前
14秒前
14秒前
Hz完成签到 ,获得积分10
15秒前
li发布了新的文献求助10
16秒前
16秒前
充电宝应助独特乖乖采纳,获得10
17秒前
17秒前
17秒前
青山发布了新的文献求助10
19秒前
kk发布了新的文献求助10
19秒前
小马甲应助百百采纳,获得10
19秒前
科研通AI6.4应助racill采纳,获得10
19秒前
無心发布了新的文献求助10
19秒前
20秒前
20秒前
后会无期完成签到,获得积分10
21秒前
hhh完成签到,获得积分10
21秒前
proudme发布了新的文献求助10
21秒前
爱听歌战斗机完成签到,获得积分20
22秒前
Hello应助kk采纳,获得10
22秒前
HLJemm发布了新的文献求助10
23秒前
23秒前
24秒前
你喜欢什么样子的我演给你看完成签到 ,获得积分10
24秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Lewis’s Child and Adolescent Psychiatry: A Comprehensive Textbook Sixth Edition 2000
Continuing Syntax 1000
Encyclopedia of Quaternary Science Reference Work • Third edition • 2025 800
Signals, Systems, and Signal Processing 510
Pharma R&D Annual Review 2026 500
荧光膀胱镜诊治膀胱癌 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6216862
求助须知:如何正确求助?哪些是违规求助? 8042251
关于积分的说明 16763429
捐赠科研通 5304265
什么是DOI,文献DOI怎么找? 2825972
邀请新用户注册赠送积分活动 1804168
关于科研通互助平台的介绍 1664170