<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study focuses on the urban landscape design of city A, and uses information fusion technology to evaluate. Through the analysis of the measured data by the entropy weighting method and the ensemble optimal weighting method, and compared with the established index system, the results reveal the weights of different landscape elements. Regarding ecological landscape, the CR value was 0.003, in which landscape diversity and green space rate were the leading indicators, and the weights were 0.2976. The weights of landscape dominance and uniformity were relatively medium, both of which were 0.1579. In the evaluation of aesthetic landscape, the CR value was 0.0309, and the highway alignment and urban landscape indicators in the urban landscape were the most important, with a weight of 0.2044, while the weight of the indication information was the lowest, only 0.0266. The CR value of the cultural landscape was 0.0518, of which the weight of scenic connectivity was the highest (0.5559), followed by the number and scale of scenic spots (0.3537), and the weight of historical and cultural elements was the lowest (0.0904). In the past few years, landscape design has accounted for the most significant proportion of landscape construction investment, especially reaching a peak of about 57,000 yuan in 2022. From 2018 to 2022, the cumulative investment in urban landscape design is about 246664 yuan.</p>
</abstract>

This study focuses on the urban landscape design of city A, and uses information fusion technology to evaluate. Through the analysis of the measured data by the entropy weighting method and the ensemble optimal weighting method, and compared with the established index system, the results reveal the weights of different landscape elements. Regarding ecological landscape, the CR value was 0.003, in which landscape diversity and green space rate were the leading indicators, and the weights were 0.2976. The weights of landscape dominance and uniformity were relatively medium, both of which were 0.1579. In the evaluation of aesthetic landscape, the CR value was 0.0309, and the highway alignment and urban landscape indicators in the urban landscape were the most important, with a weight of 0.2044, while the weight of the indication information was the lowest, only 0.0266. The CR value of the cultural landscape was 0.0518, of which the weight of scenic connectivity was the highest (0.5559), followed by the number and scale of scenic spots (0.3537), and the weight of historical and cultural elements was the lowest (0.0904). In the past few years, landscape design has accounted for the most significant proportion of landscape construction investment, especially reaching a peak of about 57,000 yuan in 2022. From 2018 to 2022, the cumulative investment in urban landscape design is about 246664 yuan.

Research on Evaluation Index System of Urban Landscape Design Driven by Information Integration Technology

Zhongyuan University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study focuses on the urban landscape design of city A, and uses information fusion technology to evaluate. Through the analysis of the measured data by...

Research on Evaluation Index System of Urban Landscape Design Driven by Information Integration Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0463

Schlüsselwörter
Information fusion technology, Entropy weight law, Ensemble optimal weighting method, Indicator system, Urban landscape design

© 2024 Yu Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on Evaluation Index System of Urban Landscape Design Driven by Information Integration Technology

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 19. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0463

SchlüsselwörterInformation fusion technology, Entropy weight law, Ensemble optimal weighting method, Indicator system, Urban landscape design

© 2024 Yu Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Information fusion technology, Entropy weight law, Ensemble optimal weighting method, Indicator system, Urban landscape design