| Title |
How to Evaluate Smart Cities' Construction? A Comparison of Chinese Smart City Evaluation Methods Based on PSF |
| ID_Doc |
37796 |
| Authors |
Shi, HB; Tsai, SB; Lin, XW; Zhang, TY |
| Title |
How to Evaluate Smart Cities' Construction? A Comparison of Chinese Smart City Evaluation Methods Based on PSF |
| Year |
2018 |
| Published |
Sustainability, 10.0, 1 |
| DOI |
10.3390/su10010037 |
| Abstract |
With the rapid development of smart cities in the world, research relating to smart city evaluation has become a new research hotspot in academia. However, there are general problems of cognitive deprivation, lack of planning experience, and low level of coordination in smart cities construction. It is necessary for us to develop a set of scientific, reasonable, and effective evaluation index systems and evaluation models to analyze the development degree of urban wisdom. Based on the theory of the urban system, we established a comprehensive evaluation index system for urban intelligent development based on the people-oriented, city-system, and resources-flow (PSF) evaluation model. According to the characteristics of the comprehensive evaluation index system of urban intelligent development, the analytic hierarchy process (AHP) combined with the experts' opinions determine the index weight of this system. We adopted the neural network model to construct the corresponding comprehensive evaluation model to characterize the non-linear characteristics of the comprehensive evaluation indexes system, thus to quantitatively quantify the comprehensive evaluation indexes of urban intelligent development. Finally, we used the AHP, AHP-BP (Back Propagation), and AHP-ELM (Extreme Learning Machine) models to evaluate the intelligent development level of 151 cities in China, and compared them from the perspective of model accuracy and time cost. The final simulation results show that the AHP-ELM model is the best evaluation model. |
| Author Keywords |
smart city; evaluation; PSF evaluation model; analytic hierarchy process; BP neural networks; extremely learning machine; sustainability; green operation |
| 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:000425082600036 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/10/1/37/pdf?version=1514186861
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