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
1秒前
1秒前
KingWave发布了新的文献求助10
1秒前
胡运恒发布了新的文献求助10
1秒前
冷静初彤发布了新的文献求助30
2秒前
大模型的应助被尹文采纳,获得10
4秒前
我又来了发布了新的文献求助10
5秒前
7秒前
谢雷XIELei的应助被TUYGO采纳,获得10
7秒前
sun关注了科研通微信公众号
8秒前
想飞的猪发布了新的文献求助10
8秒前
9秒前
9秒前
善良语风完成签到,获得积分10
10秒前
大白发布了新的文献求助10
10秒前
MM发布了新的文献求助10
11秒前
11秒前
xiqianyangyi发布了新的文献求助10
11秒前
L8完成签到,获得积分10
12秒前
爆米花的应助被想飞的猪采纳,获得10
13秒前
小蘑菇的应助被海洋之心采纳,获得10
13秒前
吴咩咩发布了新的文献求助10
14秒前
啦啦啦发布了新的文献求助10
15秒前
zhang完成签到 ,获得积分10
15秒前
ding的应助被谷子采纳,获得10
15秒前
question500发布了新的文献求助10
16秒前
习木木完成签到,获得积分20
16秒前
19秒前
19秒前
20秒前
牛马12号完成签到,获得积分10
20秒前
21秒前
21秒前
21秒前
小仓完成签到,获得积分10
22秒前
22秒前
小面包发布了新的文献求助50
23秒前
竹月逐月发布了新的文献求助10
23秒前
科目三的应助被白斯特采纳,获得10
23秒前
Ee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
Energie-Contracting 1500
Représentation chimique de la structure des kérogènes et des asphaltènes en fonction de leur origine et de leur degré d'évolution 1000
Klimaschutzrecht durch den Energiesektor im Mehrebenensystem – Ein Rechtsvergleich 1000
Oncology Clinical Trials: Successful Design, Conduct, and Analysis, Second Edition 800
Ecotourism 400
Genera Orchidacearum Volume 4: Epidendroideae 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8065254
求助须知:如何正确求助?哪些是违规求助? 9487875
关于积分的说明 21344549
捐赠科研通 7633365
什么是DOI,文献DOI怎么找? 3397459
关于科研通互助平台的介绍 2527531
邀请新用户注册赠送积分活动 2435444