| Title |
Identifying Smart City Leaders and Followers with Machine Learning |
| ID_Doc |
45038 |
| Authors |
Liu, FY; Damen, N; Chen, ZX; Shi, Y; Guan, SH; Ergu, D |
| Title |
Identifying Smart City Leaders and Followers with Machine Learning |
| Year |
2023 |
| Published |
Sustainability, 15, 12 |
| DOI |
10.3390/su15129671 |
| Abstract |
Smart cities have been a popular topic for the city stakeholders. A smart city is the next urban lifestyle that citizens expect. Due to the hypercompetitive and globalized economy, many cities have already started or are about to start their smart city projects. There is no uniform benchmark to evaluate the smart cities' performance. Several organizations use their own indicators to evaluate smart cities worldwide or nationwide. This research paper leverages fuzzy logic to label smart city leaders and followers based on various organization's evaluation meta results and then uses machine learning techniques to identify the key characteristics of leaders and followers. Based on the training data performance, the Support Vector Machine (SVM) is used to predict who will be the next smart city leader or follower. According to the proposed prediction framework, we have successfully predicted 30 smart city leaders and 20 followers. |
| Author Keywords |
smart city; fuzzy logic; machine learning; prediction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:001017729700001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
|