已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

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 物理 热力学 运营管理
作者
Adeeb A. Kutty,Tadesse G. Wakjira,Murat Küçükvar,Galal M. Abdella,Nuri Cihat Onat
出处
期刊:Journal of Cleaner Production [Elsevier]
卷期号:378: 134203-134203 被引量:41
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
口日目发布了新的文献求助10
4秒前
外向的半邪关注了科研通微信公众号
6秒前
11发布了新的文献求助10
9秒前
SOLOMON应助科研通管家采纳,获得10
9秒前
微笑世开完成签到 ,获得积分10
9秒前
HGalong应助科研通管家采纳,获得10
9秒前
10秒前
rita_sun1969完成签到,获得积分10
12秒前
huzhen完成签到,获得积分10
14秒前
钮祜禄萱完成签到 ,获得积分10
19秒前
24秒前
心灵美诗霜完成签到 ,获得积分10
30秒前
Calyn完成签到 ,获得积分10
30秒前
33秒前
35秒前
我是老大应助小伙子采纳,获得10
37秒前
小小酥完成签到 ,获得积分10
37秒前
星辰完成签到 ,获得积分10
37秒前
bobo完成签到 ,获得积分10
37秒前
hilton发布了新的文献求助10
41秒前
英俊的铭应助liuliqiong采纳,获得10
41秒前
41秒前
刘锦滔发布了新的文献求助10
44秒前
星辰大海应助口日目采纳,获得10
45秒前
YD应助shuanglin采纳,获得10
46秒前
xiaozhao发布了新的文献求助10
46秒前
48秒前
刘锦滔完成签到,获得积分10
54秒前
小二郎应助liuliqiong采纳,获得10
55秒前
hilton完成签到,获得积分10
58秒前
研友_VZG7GZ应助yjy采纳,获得10
1分钟前
正直傲霜完成签到,获得积分10
1分钟前
口日目完成签到,获得积分10
1分钟前
1分钟前
nano发布了新的文献求助10
1分钟前
张可完成签到 ,获得积分10
1分钟前
1分钟前
α(阿尔法)完成签到 ,获得积分10
1分钟前
1分钟前
研友_LNoAMn完成签到,获得积分10
1分钟前
高分求助中
请在求助之前详细阅读求助说明!!!! 20000
One Man Talking: Selected Essays of Shao Xunmei, 1929–1939 1000
The Three Stars Each: The Astrolabes and Related Texts 900
Yuwu Song, Biographical Dictionary of the People's Republic of China 700
Bernd Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
A radiographic standard of reference for the growing knee 400
Epilepsy: A Comprehensive Textbook 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2472632
求助须知:如何正确求助?哪些是违规求助? 2138675
关于积分的说明 5450474
捐赠科研通 1862624
什么是DOI,文献DOI怎么找? 926195
版权声明 562798
科研通“疑难数据库(出版商)”最低求助积分说明 495373