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Smart city article details
| Title | Urban Resilience And Livability Performance Of European Smart Cities: A Novel Machine Learning Approach |
|---|---|
| ID_Doc | 60153 |
| Authors | Kutty A.A.; Wakjira T.G.; Kucukvar M.; Abdella G.M.; Onat N.C. |
| Year | 2022 |
| Published | Journal of Cleaner Production, 378 |
| DOI | http://dx.doi.org/10.1016/j.jclepro.2022.134203 |
| Abstract | 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 Eu-ropean 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 unsuper-vised 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 per-formance of smart cities based on various supervised ML techniques. Classification models such as Naive 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 co-efficients (accuracy, Cohen's Kappa (kappa) 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. |
| Author Keywords | City resilience; Machine learning; Predictive model; Smart cities; Urban livability |
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