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
带上琳的眼睛应助任雨光采纳,获得10
1秒前
2秒前
2秒前
3秒前
同位素完成签到,获得积分10
3秒前
一番完成签到,获得积分20
4秒前
xiaodong完成签到,获得积分10
4秒前
4秒前
xiaoxiao发布了新的文献求助10
5秒前
5秒前
5秒前
5秒前
5秒前
6秒前
小童完成签到,获得积分10
6秒前
6秒前
7秒前
7秒前
xingyu发布了新的文献求助10
7秒前
小林子发布了新的文献求助200
7秒前
沉默的乐瑶完成签到,获得积分20
8秒前
Finger完成签到,获得积分10
8秒前
8秒前
tonghau895完成签到 ,获得积分10
8秒前
8秒前
8秒前
北沐完成签到,获得积分10
9秒前
阔达之卉发布了新的文献求助10
9秒前
lxt发布了新的文献求助10
9秒前
S4ndy完成签到,获得积分10
9秒前
万能图书馆应助Songyuxuan采纳,获得10
9秒前
科研彭于晏完成签到,获得积分10
9秒前
牛乘风发布了新的文献求助10
10秒前
10秒前
一番发布了新的文献求助10
10秒前
橙浮之年发布了新的文献求助10
10秒前
丑八怪完成签到,获得积分10
10秒前
10秒前
molihuakai应助鱼鱼鱼采纳,获得10
11秒前
11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
Römisch-Germanische Forschungen 1000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Green Fire Retardants for Polymeric Materials 400
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7616576
求助须知:如何正确求助?哪些是违规求助? 9192015
关于积分的说明 19698620
捐赠科研通 7189183
什么是DOI,文献DOI怎么找? 3271865
关于科研通互助平台的介绍 2434652
邀请新用户注册赠送积分活动 2266891